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DeepMind PhD Studentship in AI or Machine Learning

About the Studentship

Queen Mary University of London is inviting applications for the DeepMind PhD Studentship for September 2023. 

The DeepMind PhD Studentship programme is established at Queen Mary University of London in partnership with leading British AI company, DeepMind. 

The PhD Studentship supports and encourages under-represented groups, namely female and Black researchers, to pursue postgraduate research in AI or Machine Learning. 

The PhD DeepMind Studentship will cover tuition fees and offer a London weighted stipend of £19,668 per year minimum together with an annual £2,200 travel and conference allowance and a one-off equipment grant of £1,700.

• 3-year fully-funded PhD Studentship
• Access to cutting-edge facilities and expertise in AI
• Partnership and mentorship with DeepMind employees working at the cutting edge of AI research and technologies.

Who can apply Queen Mary is on the lookout for the best and brightest students in the fields of AI and Machine Learning. 

Successful applicants will have the following profile:

• Identify as female and/or are of Black ethnicity, each being under-represented groups in the field of Artificial Intelligence and Computer Science
• Should hold, or is expected to obtain an MSc in Computer Science, Electronic Engineering, AI, Physics or Mathematics or a closely related discipline; or can demonstrate evidence of equivalent work experience
• Having obtained distinction or first-class level degree is highly desirable
• Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths. 

We actively encourage applications from candidates who are ordinarily resident in the UK. The studentship is also open to International applicants. 

About the School of Electronic Engineering and Computer Science at Queen Mary

The PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. As a multidisciplinary School, we are well known for our pioneering research and pride ourselves on our world-class projects. We are 8th in the UK for computer science research (REF 2021) and 7th in the UK for engineering research (REF 2021). The School is a dynamic community of approximately 350 PhD students and 80 research assistants working on research centred around a number of research groups in several areas, including Antennas and Electromagnetics, Computing and Data Science, Communication Systems, Computer Vision, Cognitive Science,  Digital Music, Games and AI, Multimedia and Vision, Networks, Risk and Information Management, Robotics and Theory

For further information about research in the school of Electronic Engineering and Computer Science, please visit: http://eecs.qmul.ac.uk/research/ .

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers - whether in academia, industry or government – therefore the project undertaken under this Studentship is expected to fit into the wider research programme of School. Applicants should select a supervisor (a first and second choice) from the School at application stage. Visit our website for information about our research groups and supervisors:  eecs.qmul.ac.uk/phd/phd-opportunities/

Applicants should submit their interest by returning the following to  [email protected] by 12pm (noon), 10 April 2023:

• Indicate first and second choice academic supervisor 
• CV (max 2 pages) 
• Cover letter (max 4,500 characters)
• Research proposal (max 500 words) 
• 2 References 
• Certificate of English Language (for students whose first language is not English) 
• Other Certificates  

Application deadline: 10 April 2023

Applications will be reviewed by a panel of academic staff: May 2023

Interviews:  April/May 2023

Start date:  September 2023

How we work in artificial intelligence

Ukri artificial intelligence centres for doctoral training.

The UK Research and Innovation (UKRI) artificial intelligence (AI) Centres for Doctoral Training (CDTs) are training a new generation of PhD students. They will develop novel AI methodology and use AI technology in areas such as:

• improving healthcare
• tackling climate change
• creating new commercial opportunities

Following the £100 million investment in 2018 in 16 UKRI AI CDTs, in 2023 UKRI invested a further £117 million in 12 new centres to continue training doctoral researchers in AI across the remit of UKRI. These new centres will train around 900 students over eight years. The first cohort will start in the 2024 to 2025 academic year.

The 2023 investment supported CDTs based at 16 universities and included partnerships with 370 different organisations from the private, public and third sector. These partnerships leveraged an additional £71 million from project partners and £40 million from the universities. EPSRC has also funded CDTs relevant to AI.

If you are interested in studying at one of the CDTs or partnering with them, contact the centre directly.

Ask a question about AI CDTs

Email: [email protected]

We aim to respond in two working days.

2023 centres

The centres are:

UKRI AI Centre for Doctoral Training in Lifelong Safety Assurance of AI-enabled Autonomous Systems (SAINTS) Led by Professor Simon Parsons, University of Lincoln, with universities of Aberdeen, Strathclyde and Queen’s University Belfast

UKRI AI Centre for Doctoral Training in Responsible and Trustworthy in-the-world Natural Language Processing Led by Professor John Vines, The University of Edinburgh

UKRI AI Centre for Doctoral Training in AI for Sustainability Led by Professor Enrico Gerding, University of Southampton

UKRI AI Centre for Doctoral Training in Citizen-Centred Artificial Intelligence Led by Professor Shaun Lawson, Northumbria University

UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence (PrO-AI) Led by Professor Peter Flach, University of Bristol

UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems Led by Dr Mauricio Alvarez Lopez, The University of Manchester with University of Cambridge

UKRI AI Centre for Doctoral Training in Dependable and Deployable Artificial Intelligence for Robotics (D2AIR) Led by Professor Ronald Petrick, Heriot-Watt University with The University of Edinburgh

UKRI AI Centre for Doctoral Training in Biomedical Innovation Led by Professor Ian Simpson, The University of Edinburgh

UKRI AI Centre for Doctoral Training in AI for the Environment (Intelligent Earth) Led by Professor Philip Stier, University of Oxford

UKRI AI Centre for Doctoral Training in AI for Digital Media Inclusion Led by Professor Adrian Hilton, University of Surrey with Royal Holloway, University of London

UKRI AI Centre for Doctoral Training in Digital Healthcare Led by Professor Aldo Faisal, Imperial College London

2018 centres

UKRI AI Centre for Doctoral Training in Foundational Artificial Intelligence Led by Professor David Barber, University College London

UKRI AI Centre for Doctoral Training in AI Enabled Healthcare Systems Led by Professor Paul Taylor, University College London

UKRI AI Centre for Doctoral Training in Environmental Intelligence: Data Science and AI for Sustainable Futures Led by Professor Gavin Shaddick, University of Exeter

UKRI AI Centre for Doctoral Training in Natural Language Processing Led by Professor Mirella Lapata, The University of Edinburgh

UKRI AI Centre for Doctoral Training in Artificial Intelligence and Music Led by Professor Simon Dixon, Queen Mary University of London

UKRI AI Centre for Doctoral Training in Speech and Language Technologies and their Applications Led by Professor Thomas Hain, The University of Sheffield

UKRI AI Centre for Doctoral Training in AI for Healthcare Led by Professor Aldo Faisal, Imperial College London

UKRI AI Centre for Doctoral Training in Accountable, Responsible and Transparent AI Led by Professor Eamonn O’Neill, University of Bath

UKRI AI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing Led by Professor Gert Aarts, Swansea University with universities of Bangor, Cardiff, Aberystwyth and Bristol

UKRI AI Centre for Doctoral Training in Machine Intelligence for Nano- Electronic Devices and Systems Led by Professor Tim Norman, University of Southampton

UKRI AI Centre for Doctoral Training in Socially Intelligent Artificial Agents Led by Professor Alessandro Vinciarelli, University of Glasgow

UKRI AI Centre for Doctoral Training in Biomedical Artificial Intelligence Led by Professor Ian Simpson, The University of Edinburgh

UKRI AI Centre for Doctoral Training in Interactive Artificial Intelligence Led by Professor Peter Flach, University of Bristol

UKRI AI Centre for Doctoral Training in AI for the Study of Environmental Risks Led by Dr Emily Shuckburgh, University of Cambridge

UKRI AI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence Led by Dr Elizabeth Black, King’s College London with Imperial College London

UKRI AI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care Led by Professor David Hogg, University of Leeds

Meeting reports

UKRI AI CDT meeting reports

Last updated: 15 May 2024

This is the website for UKRI: our seven research councils, Research England and Innovate UK. Let us know if you have feedback or would like to help improve our online products and services .

Artificial Intelligence Machine Learning and Advanced Computing Postgraduate Research - 2024 Entry

Course details.

• Qualification PhD
• Duration 4 years

About This Course

One fully-funded 4-year PhD scholarship is available to start in September 2023 in the area of Artificial Intelligence machine learning and advanced computing (AIMLAC). The PhD is suitable for a graduate with a keen interest in AI algorithms for big data, optimisation, 3D modelling, and visualisation. The exciting project will research  Smart Optimisation of Big Data for Geometry Generation and 3D Models,  with Applications in Optimisation and Machine Learning of Big Data from various sources such as LiDAR (3D point clouds) obtained from architectural buildings and historical heritage.

The 4-year PhD, scholarship will sit within the  UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing  (CDT-AIMLAC). The students will be based at Bangor University, located within the  School of Computer Science and Electronic Engineering  (CSEE). Funding will cover the full cost of tuition fees and an annual stipend of approximately £15,900.  Additional funding is available for research expenses.

Candidates must be resident in the UK without any immigration restriction. Applicants are required to submit a research proposal, on this topic, and written in their own words, when they submit their application. Candidates will be shortlisted, and then invited for interview.

Additional information of the project can be found  here .

Project title:  Smart Optimisation of Big Data for Geometry Generation and 3D Models

1st supervisor:  Dr Mosab Bazargani  (School of Computer Science and Engineering)

2nd supervisor:  Prof Jonathan C. Roberts (School of Computer Science and Engineering)

The successful candidate will be required to attend the AIMLAC taught components in year 1 (such as foundations of AI, research methods, information visualisation), residential meetings at Aberystwyth, Bristol, Cardiff or Swansea Universities, deliver responsible innovation, and engage with placements with external partners throughout the four-year programme. Placements may be six-month, or shorter three-month or two-week blocks. Successful applicants will be registered at Bangor University, hosted by the School of Computer Science and Engineering throughout their period of study, with the delivery of the related training in the PhD programme being shared between the Universities of Aberystwyth, Bangor, Bristol, Cardiff and Swansea. 

Entry Requirements

Applicants should have at least a 2:1 degree. Applicants must demonstrate excellent programming skills, and have followed a suitable degree programme, e.g., in computer science, mathematics or electronic engineering (with substantial programming), or closely related discipline. Applicants must have an interest in AI, machine learning and advanced computing and one of the topics, above. Applicants must have excellent written and spoken English. Applicants should have an aptitude and ability in computational thinking and methods (as evidenced by your degree and application information). Shortlisted candidates will be interviewed around the second half of July to the beginning of August. 

To qualify as a UK applicant, prospective students must have been ordinarily resident in the UK for three years immediately prior to the start of the award, with no restrictions on how long they can remain in the UK. Overseas applicants are not eligible, as we have met our quota that is applied across the whole AIMLAC CDT cohort. 

Application

To apply for the AIMLAC funded position at Bangor University, for the 2023 intake, applicants  must  complete Bangor’s PhD Direct Application process, and include the relevant and required information as below:

Select “Apply Now” from the menu. Applicants  must  include the following information.

• One research proposal , written in their own words, and based on the topic.
• An  up-to-date CV , evidencing suitable experience for the PhD positions.
• An  accompanying letter , including a statement of no longer than 1000 words that explains (a) why you want to join our Centre, and (b) your coding experience, with examples.
• certificates  and transcripts (if you are still an undergraduate, provide a transcript of results known to date),
• Academic  references  - all scholarship applications require two supporting references to be submitted. Please ensure that your chosen referees are aware of the funding deadline (to be determined), as their references form a vital part of the evaluation process. Please include these with your scholarship application.

Applicants must also complete equality, diversity and inclusivity information. This is a requirement of the funders. Due to collaborative nature of the award, this detail must also be submitted to the AIMLAC central email, separately to the application.  C omplete the  Monitoring Equality, Diversity and Inclusivity form  at time of your BU application.

Interviews (using video conferencing or in person) will occur during the second half of July to the beginning of August. 

The deadline for applications is August 17th 2023;  with interviews planned for the week starting 21 August, with a start date of 18th September.   However applications will be accepted until all positions are filled.

For more information please contact  Professor Jonathan Roberts

Explore More in Electronic Engineering

The advent of the digital era makes electronics and electronic devices more important than ever. Our world-leading experts expose our students to cutting-edge technologies and research. Our ambitions centre around employing micro and nanotechnology to exploit new materials and techniques.

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Course type

Qualification, university name, doctorate degrees in artificial intelligence (ai).

16 degrees at 13 universities in the UK.

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• Course title (A-Z)
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• Price: low - high

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University of leicester.

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PhD Robotics and Systems Engineering

University of salford.

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University of surrey.

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Ucl (university college london).

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Postgraduate research project

Responsible and trustworthy ai, about the project.

The global artificial intelligence (AI) market is valued at over $387.4 billion and is projected to more than triple by 2029. For the world to realise the benefits brought by AI, it is important to ensure AI systems are responsibly developed, used throughout their entire life cycle, and trusted by the humans expected to rely on them.

This PhD studentship is in the broad area of responsible and trustworthy AI. Potential research topics include:

• multi-robot systems 
• autonomous systems
• multi-agent systems
• goal-driven systems
• recognition, human-computer interaction 

Modern AI technologies have great potential to advance our society. With this power comes great responsibility to ensure AI is used for good and in an equitable, transparent manner. AI has a significant impact, and ensuring that AI is designed, deployed, and used responsibly is critical to ensuring its impact is positive. 

The PhD studentship is related to our flagship £31M Responsible AI UK programme (RAI UK) . This national centre, based at the University of Southampton, will create an international ecosystem for responsible AI research and innovation. 

Potential supervisors

Lead supervisor.

Professor Gopal Ramchurn

Supervisors.

Dr Shoaib Ehsan

Research interests.

• Robotics (Localisation and Mapping)
• Embedded Systems
• Computer Vision

Entry requirements

A UK First class honours degree, or its international equivalent , in one of the following:

• computer science
• electronics
• mathematics
• a related discipline

The following are essential:

• attention to detail
• ability to work with diverse people and communities to understand their particular needs
• enthusiasm to work in the broader area of responsible and trustworthy AI to develop systems with beneficial human impact

This project is only open to UK applicants.

Fees and funding

For UK students, tuition fees will be paid and you'll receive a stipend (living allowance) of £18,622 tax-free per year for up to 3.5 years.

You need to:

• choose programme type (Research), 2023/24, Faculty of Engineering and Physical Sciences
• choose PhD in Computer science (Full time)
• add supervisor Professor Gopal Ramchurn in section 2

Applications should include:

• your CV (resumé)
• 2 reference letters
• degree transcripts to date
• a 2-page research proposal explaining what you're interested in, why it is exciting for you and what you have already read about it.

Your research idea could focus on the topics mentioned in the About the project section on the broader area of responsible and trustworthy AI. 

In your research proposal, we will be looking for your:

• excitement for your chosen topic
• enthusiasm for the opportunity to do research aligned with the Responsible AI UK programme and the UKRI Trustworthy Autonomous Systems (TAS) Hub
• ability to communicate your ideas clearly

Faculty of Engineering and Physical Sciences

If you have a general question, email our doctoral college ([email protected]) .

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• Cascading hazards from the largest volcanic eruption in over a century: What happened when Hunga Tonga-Hunga Ha’apai erupted in January 2022?
• Characterisation of cast austenitic stainless steels using ultrasonic backscatter and artificial intelligence
• Climate Change effects on the developmental physiology of the small-spotted catshark
• Climate at the time of the Human settlement of the Eastern Pacific
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• How do neutrophils alter T cell metabolism?
• How well can we predict future changes in biodiversity using machine learning?
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• If ‘Black Lives Matter’, do ‘Asian Lives Matter’ too? Impact trajectories of organisation activism on wellbeing of ethnic minority communities
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• Personalized multimodal human-robot interactions
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• The Mayflower Studentship: a prestigious fully funded PhD studentship in bioscience
• The calming effect of group living in social fishes
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• The impact of early life stress on neuronal enhancer function
• The oceanic fingerprints on changing monsoons over South and Southeast Asia
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• The role of singlet oxygen signaling in plant responses to heat and drought stress
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• Uncovering the drivers of non-alcoholic fatty liver disease progression using patient derived organoids
• Understanding recent land-use change in Snowdonia to plan a sustainable future for uplands: integrating palaeoecology and conservation practice
• Understanding the role of cell motility in resource acquisition by marine phytoplankton
• Understanding the structure and engagement of personal networks that support older people with complex care needs in marginalised communities and their ability to adapt to increasingly ‘digitalised’ health and social care
• Unpicking the Anthropocene in the Hawaiian Archipelago
• Unraveling oceanic multi-element cycles using single cell ionomics
• Unravelling southwest Indian Ocean biological productivity and physics: a machine learning approach
• Using acoustics to monitor how small cracks develop into bursts in pipelines
• Using machine learning to improve predictions of ocean carbon storage by marine life
• Vulnerability of low-lying coastal transportation networks to natural hazards
• Wideband fibre optical parametric amplifiers for Space Division Multiplexing technology
• Will it stick? Exploring the role of turbulence and biological glues on ocean carbon storage
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PhD/MSc programmes

The AI research group are heavily involved in multiple PhD and Masters programmes.

PhD Programmes

Sussex AI is offering an elite PhD programme as part of our newly established interdisciplinary centre of excellence, with 3 fully-funded (including internationals) studentships per year. Students on this programme will conduct innovative research into a range of theoretical, applied and interdisciplinary AI topics. To learn more and find out how to apply please see the Sussex AI PhD programme page .

The Sussex Neuroscience 4 year PhD programme is an elite 4 year programme with a taught first year including 3 lab rotations. It is run by Sussex Neuroscience, a university research programme, bringing together labs from across four schools. You can  learn more , read about Sussex Neuroscience and find out how to apply .

Besides the specific programmes listed above, we are always open for applications of talented students with other sources of funding. Current funding opportunities can be found on the Sussex PhD funding pages .

Masters Programmes

The study of natural and artificial intelligent and adaptive systems is at the heart of rapidly developing areas. These range from artificial intelligence and autonomous robotics to neuroscience, consciousness and cognitive science. The AIAS MSc provides a solid grounding in the major themes of the area. This well established programme (formally the EASy MSc) is taught by leading experts and there are many opportunities to interact with the thriving local community of researchers in this field.  More information is available in the AIAS course prospectus .

This programme provides advanced training in state-of-the-art AI research approaches. Modules will cover machine learning, bioinspired AI, AI research methods as well as applications and implications of AI technology. MRes students will perform an in-depth original research project, supervised by one of our world-leading researchers. This programme is suitable for students with a technical background who are already confident in the fundamentals of AI and want to learn the skills for high-quality AI research and development. More details will be made available in the next few months, for additional information please contact the convenor Dr Ivor Simpson .

This MSc programme covers the theory and practice of state-of-the-art data science with applications ranging from mathematics to physics, and from biology to computer science. While it is taught in four streams, Mathematics, Physics, Computer Science, Life Sciences, the overarching principles of how to tackle big data and the tools and techniques used to extract useful knowledge from it are surprisingly similar across the board. You will learn about these techniques, acquire the skills to apply them to the different application domains and so learn how take advantage of the explosion of data across science and society. The syllabus and other details are available in the Data Science course prospectus .

This MSc is aimed at understanding how and why data is used, and the societal and ethical implications of doing so. It relates closely to how data is collected, processed and stored throughout modern life, ranging from simple business interactions, our education, government, to our online presence on social media and otehr platforms. Taught content covers both, practical skills for getting involved with data science and reflections on the ethical implications and how to improve ethical use and interpretability. Nore details including the syllabus are in the  HSDS course prospectus .

Recent PhD Programmes

Members of the AI group were heavily involved in running two Leverhulme Doctoral Scholarship Programmes (DSP) that are no longer accepting applicants. These were prestigious, inter-disciplinary doctoral training centers with generous provisions, including for student-led activities, summer schools and research equipment.

Directors:  Prof Anil Seth  and  Prof Jamie Ward

The Leverhulme Doctoral Scholarship Programme (DSP) in ‘From sensation and perception to awareness’ is one of twelve DSPs in the UK that started in 2018, funded in a part by a generous £1M grant from the Leverhulme Trust. The programme bring together researchers from across neuroscience, robotics, psychology, and the arts, with the ultimate aim of advancing our understanding of interactions between sensing, perceiving, and awareness in humans, animal, and machines. Reflecting the interdisciplinary ethos at the heart of the programme, the 21 Leverhulme Scholars are working under the supervision of supervisors from across five of Sussex's Schools (Psychology, Informatics, Medicine, Life Sciences, and Media, Film and Music), and tackle important questions ranging from embodied music to the Neuroscience of visual perception. More details about the programme are on its  web pages , including descriptions of the  projects of the Leverhulme Scholars .

Directors:  Prof Thomas Nowotny ,  Prof Andrew Philippides  and  Prof Paul Graham

The be.AI Doctoral Scholarship Programme is supported in a part by a generous £1M grant from the Leverhulme Trust awarded in late 2020. The first intake of 6 Leverhulme Scholars will commence work on their PhDs in September 2021. They will explore how intelligence arises from the interaction of a brain with the body and the environment and what we can learn for new approaches and applications of AI. This involves many disciplines ranging from Biology to Music, Computer Science to Philosophy. There will be three intakes of students with applications open from December for a start in September the following year. You can  learn more , read about our  research themes  and find out  how to apply .

Imperial College London Imperial College London

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Artificial intelligence

Take a deep dive into the cutting edge world of machine learning and artificial intelligence

Artificial intelligence and machine learning harness computation to simulate human intelligence and solve complex problems

What is artificial intelligence?

• Jobs for the future Demand for AI professionals is growing as it permeates everyday life, from driverless cars to disease detection
• Work in almost any industry With applications in wide ranging sectors and academic fields – from medicine to business
• 1st in the UK Computer Science and Informatics Research Excellence Framework (REF) 2021 – based on the proportion of world-leading research

Why Imperial ?

Access expertise spanning theoretical foundations and applications of AI, including machine learning, robotics, and augmented reality and cognition

Benefit first-hand from research from a network of 20+ AI-related research groups and centres

Accelerate you career with a world-class learning experience delivered by leading experts

The flexibility of AI is demonstrated in the fact that a number of Imperial departments offer courses in the field. Explore our courses below to see the different ways you can apply it.

Undergraduate courses Showing courses for 2025

• Undergraduate

Biomedical Technology Ventures

Learn how to improve human healthcare through the application of technology.

Computing (Artificial Intelligence and Machine Learning)

Focus your study of computing on artificial intelligence in this integrated Master’s degree.

Computing (Visual Computing and Robotics)

Focus on the implementation and evaluation of systems that depend on computer vision or computer generated imagery.

Economics, Finance and Data Science

A first-of-its-kind degree combining economics, finance, and data science and its applications

Postgraduate taught courses

• Postgraduate taught

Applied Computational Science and Engineering

Discover how numerical methods and computational science can help solve large scale problems.

Applied Machine Learning

Learn how to design, implement and evaluate machine learning systems.

Artificial Intelligence

Receive intensive training in programming and the fundamentals of artificial intelligence.

Artificial Intelligence and Machine Learning

Develop advanced artificial intelligence and machine learning skills and apply these to real-world problems.

Artificial Intelligence Applications and Innovation

Learn AI fundamentals, explore real-world AI applications, and acquire expertise needed to become a leader in AI technologies and business

Biomedical Research (Data Science)

Interdisciplinary training in 'big data' analysis, building skills towards a career in biomedical research.

Deepen your knowledge and understanding of artificial intelligence and machine learning to prepare for a career in the computing industry.

Specialise in the study of vision, graphics, intelligent behaviour and biomedical image computing.

Digital Chemistry with AI and Automation (MSc)

Discover how advances in automation, artificial intelligence, and big data are revolutionising the study of chemistry.

Study options

• Digital Chemistry with AI and Automation (PG Cert)

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• Digital Chemistry with AI and Automation Save to Course Compare

Environmental Data Science and Machine Learning

Apply computing techniques and machine learning to real-world environmental problems.

Environmental Engineering with Data Science

Explore environmental engineering practices and the use of computational methods and statistical modelling techniques.

Geo-Energy with Machine Learning and Data Science

Develop the essential data science and machine learning skills needed to embark on a career as a geoscientist or engineer.

Geotechnical Engineering with Data Science

Study key areas of geotechnical engineering and specialise in analysing large volumes of geotechnical data to provide efficient, sustainable, resilient geotechnical solutions.

Health Data Analytics and Machine Learning (MSc)

Develop skills in using cutting-edge quantitative methods to fully exploit complex data.

Human and Biological Robotics

Advance your knowledge on how bioengineers are leading the way in tackling modern healthcare challenges.

• Postgraduate taught, Online

Machine Learning and Data Science (Online)

Develop an in-depth understanding of machine learning models and learn to apply them to real-world problems.

Nanomedicine and Nanodiagnostics MRes

Investigate the unique properties of nanoscale materials for the treatment, detection, visualisation and prevention of diseases.

Statistics (Data Science)

Build familiarity with a wide range of data science techniques and their application in a variety of settings.

Transport with Data Science

Specialise in analysing large volumes of transport data and in developing models for efficient, sustainable, and resilient transport systems.

Online courses

View our 2024 entry undergraduate courses .

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• Internal wiki

PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD

London, Bloomsbury

Artificial Intelligence (AI) has the potential to transform health and healthcare systems globally, yet few individuals have the required skills and training. To address this challenge, our Centre For Doctoral Training (CDT) in AI-Enabled Healthcare Systems will create a unique interdisciplinary environment to train the brightest and best healthcare artificial intelligence scientists and innovators of the future.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

Applications closed

The Centre for Doctoral Training recruits in at least two rounds. Applicants are advised to apply early, priority will be given to those who have applied in round one.

• Entry requirements

A minimum of an upper second class honours undergraduate degree, or a Master's degree in a relevant discipline (or equivalent international qualifications or experience). Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics) or Clinical / Biomedical Science. Applicants with a clinical background or degree in Biomedical Science must be able to demonstrate strong computational skills. You must be able to demonstrate an interest in creating, developing or evaluating AI-enabled Healthcare systems.

The English language level for this programme is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance . This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

Every student who is accepted onto the AI-enabled Healthcare Systems Centre for Doctoral Training (CDT) must take the MRes Artificial Intelligence Enabled Healthcare in their first year. This will be followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the need for continuing training and support, tailored to each student, and provide ongoing training in research skills. The MRes is not currently available as a stand-alone programme.

The MRes programme covers the core competencies of artificial intelligence and has a central emphasis on how healthcare organisations work. Ethical training for medical artificial intelligence will be explicitly emphasised alongside a broader approach to responsible research, innovation and entrepreneurship.

During the MRes year, students will learn the statistical underpinnings of machine learning theory, get a practical grounding in research software engineering and the principles of healthcare and medical research, as well as a thorough treatment of topics in machine learning, advanced statistics and principles of data science.

As part of the MRes, alongside the core and elective modules, you will complete a substantial Masters-level project of your choice, working with a supervisory team that will normally include a clinician and an academic. The project you work on during your MRes normally leads to the chosen PhD research topic.

The remaining years will be more like a traditional PhD, which leads to the presentation of a PhD thesis at the end of the fourth year. During your PhD you will remain involved in CDT activities and will continue to work closely with relevant health professionals and clinical teams through our NHS partners and leading academics at UCL.

As a cohort based PhD programme, students will also have the opportunity to participate in a range of seminars, training programmes, placements and other activities, including UCL's Doctoral Skills Development Programme.

Training Opportunities The CDT programme consists of a range of activities and events including:

• A Mini-MD programme where trainees undertake an immersive clinical experience within an NHS setting
• Annual CDT Conference
• Seminar series
• PPI Training
• Responsible Research & Innovation
• Communication Skills
• Entrepreneurship
• Ethical Training
• The opportunity to attend training programmes offered by the Alan Turing Institute
• Opportunities for internships and placements with industry partners

More information can be found on the CDT Website .

Who this course is for

The Centre for Doctoral Training programme is for students with an interest in creating and developing AI solutions aimed to transform and solve healthcare challenges. The CDT programme is embedded within a NHS setting, and should appeal to students keen to develop clinical knowledge and algorithmic/ programming expertise.

What this course will give you

• Benefit from UCL's excellence both in computational science and biomedical research innovating in AI;
• Be supervised by world-leading clinicians and AI researchers in areas related to your research;
• Work within a real-world setting, embedded within hospitals, allowing you to gain a practical understanding of the value and limitations of the datasets and the translational skills required to put systems into practice;
• Have the opportunity to not only apply AI to healthcare but to apply healthcare to AI, generating novel large-scale open datasets driving methodological innovation in AI;
• Become a future leader in solving the most pressing healthcare challenges with the most innovative AI solutions;
• Study at UCL, which is rated No.1 for research power and impact in medicine, health and life sciences (REF 2021) and 9th in the world as a university (QS World Rankings 2024).

The foundation of your career

We do not yet have any graduates from the four-year programme, our first cohort of students will be graduating over the next few months. We expect them to stay within the field of AI and healthcare, and much like previous graduates from our experienced CDT supervisors, they will go onto successful careers in academia and industry. 

Employability

The distinctive characteristics of our programme allow us to produce graduates who are prepared to:

• engineer adaptive and responsive solutions that use AI to deal with complexity;
• innovate across all levels of care, from community services to specialist hospitals;
• be comfortable working with patients and professionals, and responding to their input;
• appreciate the importance of addressing health needs rather than creating new demand.

The Institute's research departments collaborate with third-sector and governmental organisations, as well as members of the media, both nationally and internationally to ensure the highest possible impact of their work beyond the academic community. Students are encouraged to do internships with relevant organisations where funding permits. Members of staff also collaborate closely with academics from leading institutions globally.

Teaching and learning

Various teaching and learning methods are employed to facilitate effective learning and cater to different learning styles. Below are some common types of teaching methods that may be used across the programme:

Interdisciplinary Teaching: Interdisciplinary teaching involves integrating knowledge and skills from multiple disciplines or subject areas to provide a comprehensive understanding of a topic, particularly AI and healthcare. This approach encourages students to make connections between different subjects and fosters critical thinking and problem-solving abilities.

Lecture-Based Teaching: Lecture-based teaching is a traditional method where the instructor presents information to students through spoken words. It involves the teacher sharing knowledge, concepts, and theories, while students take notes and listen actively. This method is effective for conveying large amounts of information and providing foundational knowledge.

Practical Coding Sessions: Practical coding sessions are hands-on learning experiences where students actively engage in coding exercises, programming tasks, and problem-solving activities. These sessions are essential for AI and programming-related subjects (machine learning, etc) as they allow students to apply theoretical knowledge to real-world scenarios.

Interactive Teaching: Interactive teaching methods encourage active participation and engagement from students. These methods can include discussions, debates, group activities, and case studies, in particular in several modules such as Journal Club. Interactive teaching fosters collaboration, communication skills, and a deeper understanding of the subject matter.

Project-Based Learning: Project-Based Learning involves assigning students long-term projects that require them to investigate and address real-world problems or challenges (such as AI & healthcare group project). It enhances critical thinking, research skills, and creativity while promoting independent learning and teamwork.

Collaborative Learning: Collaborative learning involves students working together in small groups or pairs to solve problems, discuss ideas, and complete tasks. This method promotes teamwork, communication, and the exchange of diverse perspectives.

The use of these teaching/learning methods can vary depending on the subject matter, the goals of the programme, and the preferences of the instructors in the MRes year. Our educational programme incorporates a mix of these methods to cater to the diverse needs of learners and create a well-rounded learning experience.  

Compulsory Modules:

CHME0033 Dissertation in Artificial Intelligence Enabled Healthcare

CHME0032 Healthcare Artificial Intelligence Journal Club

Optional Modules

CHME0012 Principles of Health Data Science

CHME0013 Data Methods for Health Research

CHME0015 Advanced Statistics for Records Research

CHME0016 Machine Learning in Healthcare and Biomedicine

CHME0031 Programming with Python for Health Research

CHME0034 Computational Genetics of Healthcare

CHME0035 Advanced Machine Learning for Healthcare

CHME0039 Artificial Intelligence in Healthcare Group Project

COMP0084 Information Retrieval and Data Mining

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.

Assessment methods are crucial components of an educational programme, as they evaluate students' understanding, knowledge, skills, and application of concepts. Here are various types of assessment methods that may be used across the programme:

Exams: Traditional exams are a common assessment method that tests students' knowledge and understanding of the course material. These exams typically involve a time-bound written assessment, where students respond to questions related to the subject matter.

Open-Book Exam: In an open-book exam, students are allowed to refer to their textbooks, notes, or other resources during the assessment. The questions in these exams are often designed to test higher-order thinking and problem-solving abilities, as students have access to reference materials.

Coursework: Coursework assessments involve various assignments, essays, reports, or projects that students complete throughout the course. These assessments may cover specific topics or practical applications and help to assess students' comprehension and critical thinking skills.

Coding Exam: A coding exam is specifically designed for courses related to computer science, software development, or programming. Students are given coding challenges or programming tasks that assess their coding proficiency and problem-solving abilities.

Collaborative Project: In a collaborative project assessment, students work in groups to tackle a complex problem or complete a substantial task. This assessment measures teamwork, communication, time management, and the ability to achieve shared goals.

Presentation and Q&A: Presentations require students to deliver a talk on a given topic or project. The presentation assesses their ability to communicate effectively, organize information, and present ideas coherently. Often, a question and answer (Q&A) session follows the presentation to delve deeper into the topic.

Research Proposal: A research proposal is a preliminary plan for a research project that students submit to demonstrate their research capabilities. It outlines the research question, objectives, methodology, and potential outcomes of the study.

Dissertation Writing: Dissertation writing is typically reserved for higher education levels, such as undergraduate and postgraduate studies. It involves an extended research project on a specific subject, allowing students to demonstrate research, analytical, and academic writing skills.

Online Quizzes and Tests: Online quizzes and tests are digital assessments that may be used for formative or summative purposes. They are often employed in blended or online learning environments.

The use of assessment methods will vary based on the nature of the programme, the subject matter throughout the MRes year. A well-balanced combination of assessment types ensures that students' diverse abilities and learning styles are appropriately evaluated while providing a comprehensive understanding of their progress and achievements.

During the MRes 4 hours of a student's time is spent in tutorials per week and/or, 6-8 hours in lectures per week, and a further 20-24 hours in independent study per week.

Research areas and structure

• AI-enabled diagnostics or prognostics
• AI-enabled operations
• AI-enabled therapeutics
• Public Health Data Science
• Machine Learning in Health Care
• Public Health informatics
• Learning health systems
• Electronic health records and clinical knowledge management
• e-health and m-health
• Clinical Decision Support Systems

Research environment

Our research environment offers a unique degree programme that stands out among competitors. We provide students with the exceptional opportunity to explore the cutting-edge intersection of AI technology and healthcare applications. Our curriculum emphasizes research and innovation skills, empowering students to become independent researchers and adept problem solvers. A key difference is our close collaboration with clinicians and front-line practitioners. This interaction fosters a holistic understanding of healthcare challenges and real-world applications, ensuring that our graduates are equipped with practical knowledge and solutions. Our programme is inclusive, welcoming students from both computational and clinical backgrounds, creating a diverse and dynamic learning environment.

Students studying the programme full-time will be expected to complete 180 credits during the academic year. 

Students studying the programme part-time will be expected to complete 180 credits across two academic years. 

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

All studentships include a research training support grant, which covers additional research costs throughout students' time on the programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

Please visit the CDT website for funding information.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

Note for applicants: When applying on UCL Select, please select MRes Artificial Intelligence enabled healthcare to apply for programme.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Got questions? Get in touch

Institute of Health Informatics

[email protected]

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Student News

Tuesday, 28 may, search for news, browse student news stories.

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Fully Funded PhDs in Data Science, AI and Machine Learning

The University of Liverpool’s Centre for Doctoral training in Distributed Algorithms (CDT) are currently looking for students to join their fully funded PhDs in Data Science, AI and Machine Learning.

The team aim to develop 60 PhD students to meet the world’s pressing need for highly-trained data scientists and work with industry and government to help solve real-world problems.

Applicants come from a range of subjects and backgrounds, including:

• Computer Science
• Department of Civil Engineering and Industrial Design
• Earth, Ocean and Ecological Sciences
• Electrical Engineering and Electronics
• Geography and Planning
• Mathematical Sciences
• Mechanical and Aerospace Engineering

The fully funded PhD studentships are open to home and international students. You’ll be working as part of a cohort in a collaborative environment alongside other PhD students, postdoc researchers and data scientists. Other benefits include:

•  PhD projects co-defined and co-supervised with a project partner
•  Monthly tax-free payment of £1,338.50
•  Annual research grant
•  Placements in year 3
•  Long-term employment potential
•  Inclusive and supportive cohort environment
•  Technical, professional and personal training and development
•  Access to state-of-the-art high-performance computers

Interested?

The team would love to hear from you. Please do get in touch to find out more.

Email Kelli or Sara ( [email protected] ) if you have any questions.

They will also be at the Careers Studio on Friday 22 July between 11am – 1pm – drop-in to speak to the team, no appointment necessary.

Click here to find out more and apply

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• Click here to discover what our current students are working on and who with.
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Faculty of Science Doctoral Training Centre in Artificial Intelligence

 The  Faculty of Science  AI DTC is a new initiative by the University of Nottingham to train future researchers and leaders to address the most pressing challenges of the 21st Century through  foundational and applied AI research on a cohort basis.  The training and supervision will be delivered by a team of outstanding scholars from different disciplines cutting across Arts , Engineering , Medicine and Health Sciences , Science and Social Sciences .

The Faculty of Science will invite applications from Home students for fully-funded PhD studentships to carry out multidisciplinary research in the world-transforming field of artificial intelligence. The PhD students will have the opportunity to:

• Choose from a wide choice of AI-related multidisciplinary research projects available, working with world-class academic experts in their fields;
• Benefit from a fully-funded PhD with an attractive annual tax-free stipend;
• Join a multidisciplinary cohort to benefit from peer-to-peer learning and transferable skills development.

The deadline to have completed and submitted your application to NottinghamHub has now closed.

Research Topics

Rooted in the exceptional research environments of our Schools/Faculties at the University of Nottingham, the third cohort of the AI DTC will be organised around seventeen multidisciplinary research topics. It is important that you identify a research topic aligned with the expected skill set, your background and particular areas of interest. You will need to obtain support from the supervisors associated with your research topic choice before submitting your official application. You can do this by exploring the research projects below and contacting the main supervisor of the project that is of interest to you, directly, to discuss the further details and to arrange an interview as appropriate. In your PhD studentship application, you will be asked to provide your CV, and a personal statement including a research/project topic from the following list and explaining why you are interested in that research/project topic and your motivation for doing a PhD, and the names of the supervisors you have support from.  We encourage applicants to complete the personal statement in their own words based on their background and experience. Please follow the instructions above on how to apply.

AI for additive manufacture of complex flow devices

AI based generative methods can allow us to generate designs of structures with optimal or new functionality. These structures are often complex, which means it is difficult to manufacture them, but with additive manufacturing and its design freedoms such constraints do not apply. Consequently, the combination of AI, numerical methods and additive manufacturing can lead to a new paradigm in functionality, with any device dependent on shape for its function being available for augmentation in behaviour. 

Our approach is to use recent developments in generative design, such as AI diffusion models, to identify designs that match the requirements of the user, allowing us to ‘dial up’ or select function and have the design provided to us. The building and training of this model will be via numerical or computational models, validated by experiment, that will allow us to provide training data and indeed, augment that with data over the course of the PhD. As a final part of the jigsaw, additive manufacturing built parts will be used to validate both the numerical models and the AI model. 

Our initial focus will be on the automatic generation of mixing devices, commonly used for process intensification and downstream processing in multiple high value manufacturing operations such as pharmaceutical and chemical synthesis, but a general methodology is sought that can be applied across multiple applications. 

This project will in part be supported by a Programme Grant hosted by the University of Nottingham ‘Dialling up performance for on demand management’, with over 10 industrial partners supporting the projects. The studentship will have access to resources (physical, chemical and financial) to support the research, and also access to an exchange fund that enables extended research visits to collaborative institutions, including UC Berkeley, ETH Zurich and CSIRO.

Supervisors: Prof Ricky Wildman (Faculty of Engineering) [email protected] , Dr Mirco Magnini  (Faculty of Engineering), Prof Ender Ozcan (School of Computer Science).

For further details and to arrange an interview please contact Prof Ricky Wildman (Faculty of Engineering) [email protected]

AI-based decoding of evoked neural activity to study bilingual language processing

There are more people in the world who can speak two or more languages fluently than people who speak only one language. How are the languages of these bilinguals represented and processed in their brain? Traditionally, neuroimaging and behavioural techniques have been used to develop theories about language storage and processing. Recent studies have shown that insight about how multiple languages are represented in the brain can be gained from decoding language from neural activity. A particular interesting approach is cross-language decoding, which involves decoding brain activity of words in one language and use that to decode words of another language. Whereas most studies in the literature have used functional magnetic resonance imaging (fMRI) data for decoding language, recent studies have shown that decoding language from evoked non-invasive electroencephalography (EEG) data is also possible. However, decoding accuracies obtained have generally been low. 

This project aims to study the time course of the activation of within- and between-language representations in the bilingual brain by decoding evoked EEG activity across multiple modalities (visual and auditory). In particular, the project will focus on semantic representations. The project will involve designing and conducting EEG experiments with bilinguals to obtain data for decoding. Furthermore, the project will make use of state-of-the art machine learning techniques and advanced large language models to improve decoding accuracy. 

Applicants are expected to have a degree in Psychology, Mathematics, Computer Science, Physics, or related areas, and have knowledge of programming and a strong interest in machine learning, cognitive neuroscience, and psycholinguistics. Experience with EEG and experimental techniques are desirable, but not essential. 

Supervisors: Dr Walter van Heuven (School of Psychology), Dr Matias Ison (School of Psychology), Dr Ruediger Thul (School of Mathematical Sciences) [email protected] .

For further details and to arrange an interview please contact Dr Walter van Heuven (School of Psychology). 

AI-based digital twins and active control in composites manufacturing

 Applications are invited for a PhD studentship to conduct interdisciplinary research in the rapidly evolving area of AI for manufacturing of fibre-reinforced polymer composites. A student working on this project will develop and test novel AI algorithms for digital twins of manufactured composite parts and for active control of composites manufacturing processes. The project will include developing effective surrogate models (e.g., via physics-informed neural networks (PINNs)) and active learning of underlying physical processes. The algorithms will be used for on-the-fly estimation of properties of composites parts and for real-time active control to produce parts according to specification and avoiding defects. The project will include working with real data from experiments conducted in our Composites Research lab at the Faculty of Engineering and a range of our industrial partners. 

Supervisors: Prof Michael Tretyakov (School of Mathematical Sciences) , Dr Mikhail Matveev (Faculty of Engineering), Dr Marco Iglesias (School of Mathematical Sciences). 

For further details and to arrange an interview please contact Prof Michael Tretyakov (School of Mathematical Sciences).

Artificial-Intelligent-Aided Energy Management for Future Electric Aircraft

The COP26 has set a clear goal of securing global net zero by mid-century and keep 1.5oC within reach. To deliver this target , it is critical that the transportation sector speeds up the electrification process. For Aviation, the Intergovernmental Panel on Climate Change has set the target of at least 50% reduction of CO2 emissions by 2050 within which aviation is responsible for about 20% of overall emissions. Hybrid/Electric aircraft, which are powered by batteries, fuel cells and generators, are with great potential to reduce the CO2 emission in the Aviation Sector. 

This PhD project aims to optimise energy management on board future electric aircraft using the recently advanced machine learning and artificial intelligent (AI) technologies. The student will be working within two world-leading research groups in the University of Nottingham. The applicant should have electrical engineering background with strong math skills. Basic AI knowledge would be expected from applicant. 

Supervisors: Dr. Tao Yang (Electrical Engineering),  Dr Grazziela Figueredo (Faculty of Medicine & Health Sciences) [email protected]  

For further details and to arrange an interview please contact Dr. Tao Yang (Electrical Engineering).

Camera-based movement analysis to provide real-time optimisation of therapeutic non-invasive neuromodulation for neurological diseases: A Human-in-the-Loop and Machine Learning Approach

Involuntary movements, such as tremor, tics, sudden jerks (chorea) and muscle spasms (dystonia) occur in many neurological disorders. These involuntary movements are difficult to treat, requiring medications (which often have unpleasant side effects) or invasive deep brain stimulation (requiring electrodes to be implanted in the brain). Recent innovative research by Prof Stephen Jackson (UoN Psychology) found that non-invasive median nerve stimulation (MNS), which involves stimulating a nerve in the wrist using a wearable electronic device, suppresses involuntary movements in Tourette syndrome by entraining oscillations in relevant brain circuits when the device is active. Clinical trials are now underway, or being developed, to test the therapeutic effect in a number of neurological disorders, including Parkinson disease (PD) ataxia telangiectasia (A-T) and restless legs syndrome (RLS). 

However, involuntary movements are intermittent and highly variable, meaning that continuous stimulation by the MNS device is not required or desirable. Real-time detection of involuntary movements could allow personally-tailored therapeutic stimulation strategies. optimised to detect different types of involuntary movements seen in diseases such as PD, AT and RLS, and used to optimise the stimulation regime on an individual basis. 

In this project, we will analyse videos of trial participants to understand how MNS affects the patients' symptoms, and to optimise the device in real time to provide the maximum benefit for each individual. To achieve this, we will utilise state-of-the-art marker-less pose estimation in combination with multi-modal machine learning to better understand the complex movements of those with movement disorders. This will enable us to tailor the treatment to each individual's specific needs in real time and maximise its effectiveness by tuning the MNS device. Demonstrating the feasibility of the human-in-the-loop approach will directly enable clinical trials of the effectiveness of personalised home-administered MNS stimulation in reducing the unwanted movements, and allow exploration of the potential benefits of this technology in improving quality of life for individuals with movement disorders. 

This PhD project will benefit from a strong multidisciplinary approach, combining computer science, psychology, and neuroscience. Applicants are expected to have a combination of programming experience (python) and an interest/background in neuroscience. 

Supervisors: Dr Alexander Turner (School of Computer Science), Prof Stephen Jackson  (School of Psychology), Prof Robert Dineen (Faculty of Medicine & Health Sciences) 

For further details and to arrange an interview please contact Dr Alexander Turner  (School of Computer Science).

Computer vision for dynamic materials analysis

Electronic materials development is reliant upon the use of computational chemistry tools for elucidating structure-property relationships on the atomic scale. Molecular dynamics simulations can capture macroscopic material changes involving over 100 million atoms, such as catastrophic electrical breakdown, whilst retaining information down to the level of individual atoms. As electronic device miniaturisation has reached the nanoscale, using dynamic simulations as a theoretical microscope will be vital for overcoming the materials bottleneck we are facing at the end of Moore’s Law. 

One of the challenges in calculations of this scale is the analysis of structural changes including crystallinity, grain boundaries and nanodomain formation. These features are critical in determining properties from electronic and thermal conductivity to optical processes and response to electric fields. However, their complex dynamical behaviour involves thousands of atoms moving over hundreds of thousands of timesteps, making them unsuited to traditional modes of analysis developed for regular crystalline materials. 

This project will take advantage of AI tools in computer vision as a new approach to analyse structural features in dynamical materials simulations. These have advanced to enable analysis of images of self-similar repeating patterns, which will be highly applicable to subtle changes in atomic configurations. Deep learning methods will be used to develop classification, segmentation, and regression models to identify structural transitions, anomaly detection to identify point defects, and edge detection for revealing grain boundaries. These will be applied to the most pressing materials chemistry problems hindering electronic device miniaturisation. 

Potential applicants are expected to have some background in machine learning and/or computational materials/chemistry, in addition to some experience in (and desire to learn) scientific programming such as Python, scikit-learn, and relevant deep learning libraries, such as PyTorch. 

Supervisors: Dr Katherine Inzani (School of Chemistry), Dr Valerio Giuffrida (School of Computer Science), Dr Julie Greensmith (School of Computer Science)

For further details and to arrange an interview please contact Dr Katherine Inzani (School of Chemistry).

Computer Vision-Based Monitoring of Equine Welfare and Wellness

We are seeking a dynamic and passionate PhD student to join our team and help drive enhancements to equine health and welfare through AI based monitoring. Through this PhD, you will have the unique opportunity to gain an exceptional skillset through interdisciplinary collaboration between the UoN School of Veterinary Medicine and Science, School of Computer Science, and industry partner Vet Vision AI; helping to shape the future of the animal health and welfare. 

Numerous potential welfare issues impact equine welfare when stabled. However, many of these are preventable. Current processes to improve welfare are extremely limited, often relying on owner “know-how” rather than objective and continuous monitoring. The use of AI to automatically monitor welfare outcome will provide a step change in animal health and welfare monitoring, empowering vets and horse owners to revolutionise equine welfare monitoring. 

This PhD aims to develop and deploy cutting-edge computer vision algorithms alongside veterinary insights to monitor and improve equine welfare outcomes accurately and automatically. Successful applicants will be based in the School of Veterinary Medicine, with a close-knit team environment within which mentoring, collaboration and idea sharing is strongly promoted, with input from world-leading computer scientists from the School of Computer Science. They will also work directly with experienced computer vision developers alongside partner company Vet Vision AI; a spinout from the School of Veterinary Medicine and Science on a mission to revolutionise animal health and welfare by combining veterinary insights and computer vision technology. 

This PhD aims to combine world leading veterinary expertise and equine domain knowledge with advanced skills in computer vision and machine learning. Their aim is to translate this knowledge into cutting edge solutions that will help owners and veterinary surgeons improve the lives of millions of animals worldwide. 

Supervisors: Supervisors: Dr Robert Hyde (School of Veterinary Medicine and Science), Prof Sarah Freeman (School of Veterinary Medicine and Science), Dr Katie Burrel (School of Veterinary Medicine and Science), Dr Zhun Zhong (School of Computer science),   Prof Andrew French  (School of Computer Science) 

For further details and to arrange an interview please contact Dr Robert Hyde .

Energy requirements of neuromorphic learning systems

Large neural networks are rapidly invading many parts of science, and have yielded some very exciting results. However, particularly the training of large networks requires a lot of energy. This energy is needed to compute, but also to store information in the synaptic connections between neurons. Interestingly, also biological systems require substantial amounts of energy to learn. Under metabolically challenging conditions, these requirements can be so large that in small animals learning reduces the lifespan. Based on these findings we have started to design algorithms that reduce the energy needed to train neural networks. 

This project will explore energy requirements for learning in neuromorphics system with memristors. Neuromorphic systems mimic the biological nervous system in their design principles and are currently being explored to create highly energy efficient neural networks. Memristors that are a key technology in such devices. Specifically, we will 1) develop models that describe the energy needs for learning in neuromorphic networks, 2) use inspiration from biology to design efficient algorithms that are more energy efficient and test them in simulations, and 3) contrast energy requirements to the energy needs in biology as well as conventional hardware. 

The ideal applicant will have a strong background in physics, mathematics, computer science or engineering with both analytical and programming skills. Interest in biology and/or engineering will be beneficial. 

Supervisors: Prof Mark van Rossum (School of Psychology), Dr Neil Kemp (School of Physics and Astronomy) [email protected] .  

For further details and to arrange an interview please contact Prof Mark van Rossum (School of Psychology).

Imaging Molecules in Action: A Journey from Atoms to Energy Materials

Over 300 years scientists have devised many ways to illustrate atoms and molecules and their behaviour, using everything from elemental symbols to sophisticated 3D models. It’s easy to think these representations are reality, but how we picture molecules is usually based on bulk measurements where information is averaged over billions of billions of molecules (spectroscopy) or reciprocal space (diffractometry). Recently, electron microscopy began revolutionising the way we see matter and allowed us to glimpse at atomically resolved images of individual molecules. Early breakthroughs are exciting, but they raised three key questions: 

• What does it mean to ‘see a molecule’? 

• How can we reconstruct the 3D shape of a molecule from 2D micrographs? 

• How can we achieve resolution in space and time for tracking the movement of individual molecules? 

In this project, we aim to answer fundamental questions related to electron microscopy by advancing image analysis methods. We plan to make a significant step-change by applying advanced image analysis methods (Xin Chen) to problems related to single-molecule imaging (Andrei Khlobystov). While sub-Angstrom resolution can be achieved routinely, there's a pressing need for robust techniques to denoise electron microscopy images that allow comparing experimental and theoretically simulated images. For example, image denoising using a diffusion probabilistic model has the potential to identify atom positions while simultaneously enhancing the temporal resolution of electron microscopy. This breakthrough in spatiotemporal continuity of atomic imaging could allow us to watch molecules in action and film chemical reactions with atomic resolution in real-time. 

The Nanoscale & Microscale Research Centre at Nottingham, along with leading European centres in Ulm, Germany and Diamond Light Source in the UK, will provide training for the student to image molecules with atomic resolution. In addition, the student will learn to develop modern artificial intelligence algorithms for image processing and analysis to enhance the data to the highest level of spatiotemporal resolution. 

The project aims to revolutionize the imaging of molecules, turning it into a valuable tool for discovering new chemical processes. This will be particularly useful in catalysis and net-zero technologies, which are crucial for 70% of the UK chemical industry. The University of Nottingham holds the largest EPSRC grant in this area (EP/V000055/1). 

Supervisors: Prof Andrei Khlobystov (School of Chemistry), Dr Xin Chen (School of Computer Science).  

For further details and to arrange an interview please contact Prof Andrei Khlobystov (School of Chemistry).

Intelligent Modelling of RNA Nanomedicines using Cryo-OrbiSIMS, Machine Learning and Molecular Simulations

Motivation: Imagine a new virus emerges, causing a global disease outbreak. Scientists quickly sequence the virus's genome, but developing a traditional vaccine or treatment can take months to years, and a faster solution is needed to control the outbreak. Our project addresses this critical challenge by developing an innovative approach to aid rapid and intelligent design of RNA nanomedicines. 

Challenges to be addressed: Designing RNA-based nanomedicines, such as mRNA vaccines, presents a unique challenge for computational scientists. RNA molecules adopt complex 3D folds that significantly impact their function within a cell. For instance, the way the mRNA folds within the vaccine can affect how well the body can read its instructions and elicit an effective immune protection. Thus, it is important to determine the 3D structure of the mRNA within the vaccine. However, current experimental and computational techniques struggle to resolve its 3D architecture and flexibility. 

To address this technology gap, we have pioneered the integration of cryo-OrbiSIMS, a unique mass spectroscopic imaging capability of University of Nottingham, with molecular modelling and computer simulations to model the three-dimensional structures of RNAs at atomic resolution. 

However, studying RNA nanomedicines, such as mRNA vaccines and viral vectors, using our method presents two specific challenges: 

1. The cryo-OrbiSIMS data becomes significantly complex and hinders efficient data analysis using traditional computer programming. 

2. The RNA molecules adopt increasingly complex folds and interactions, which are challenging to model using 3D structure prediction algorithms. 

Proposed solution: Through this AI DTC project, we aim to use AI/ML techniques to learn the complex relationships between RNA structures and its corresponding cryo-OrbiSIMS data to enhance our data analysis and interpretation pipelines. 

Skillsets, Training and Development: The project sits at an interdisciplinary interface of structural biophysics and computational sciences. Thus, a strong background in data handling, statistical analysis or computer programming would be essential in the DTC candidate. Alternatively, experience in computational structural biology would be desirable. 

The core-supervisory team for this project comprises of experts from RNA biology, structural biology, mass spectroscopy and computer science. Thus, they are very well suited to provide a cross-institutional, dynamic, intellectually stimulating and highly resourceful research environment. The doctoral student will be based within the Wolfson Centre for Global Virus Research at University of Nottingham with supervisory links to the School of Pharmacy, School of Computer Science and Nanoscale and Microscale Research Centre, where a number of PhDs, postdocs and technical staff will provide day-to-day assistance in state-of-art, interdisciplinary techniques. 

Supervisors: Dr Aditi Borkar , (School of Veterinary Medicine and Science), Dr Grazziela Figueredo (Faculty of Medicine & Health Sciences), Dr David Scurr (School of Pharmacy), Prof Morgan Alexander  (School of Pharmacy), Dr Anna Kotowska (School of Pharmacy).

For further details and to arrange an interview please contact Dr Aditi Borkar , (School of Veterinary Medicine and Science). 

"Learning by Doing": A Cross-Disciplinary Exploration of Human Motor Learning, AI, and Robot Learning

AI and Robotics technologies are becoming pervasive in everyday human life, e.g. telepresence robots, robot vacuums, humanoids in the factories, etc. To operate in and interact with humans and made-for-human environments, robot learning plays a vital role, allowing robots to be more easily extended and adapted to novel situations. An example of applying AI and machine learning to robotics is learning-by-demonstration, a supervised learning technique, where robots acquire new skills by learning to imitate an expert. 

The research challenge lies in developing robot learning models by modelling the different stages of motor learning in humans, i.e., modelling humans as naïve learners, rather than experts. The fundamental research in human motor learning (HML), by which humans acquire and refine motor skills through practice, feedback, and adaptation, proves critical in this regard. This can allow the robot to mimic human motor control mechanisms, such as joint flexibility and compliance, more naturally, with the benefit of refining the acquired skill over time and input from humans. Example applications include: an assistive robotic arm assisting stroke patients guide their movements and providing corrective cues; therapy patients learning to adjust their motor patterns for better outcomes, based on the AI models, etc. 

This research will address the challenge noted, focussing on three measurable outcomes – (i) novel research into AI to model HML in subjects, including adults and children, especially with the goal of mapping the HML model to robot motion; (ii) implementing the developed HML models in a robot learning architecture (in robotic manipulation or navigation) evaluated against novel benchmarking metrics (to be investigated) for the applicability and utility of developed research in real-world; and (iii) annotated and labelled dataset (videos, sensor data, etc.) of human as well as robot motions made publicly available, for the benefit of the scientific communities in psychology, AI, and robotics. 

Prospective PhD applicants may have a degree in Computer Science or Robotics (or Psychology with an experimental focus), and with knowledge of Machine Learning, Deep Learning, AI, and Robotics (preferably). This project will require excellent programming skills with evidence of proficient working knowledge in one or more of the following: C++, Python, ROS. 

Supervisors: Dr Nikhil Deshpande (School of Computer Science), Dr Deborah Serrien (School of Psychology) [email protected] .

For further details and to arrange an interview please contact Dr Nikhil Deshpande (School of Computer Science). 

Learning Heuristics for Computer Algebra Software

Computer Algebra Systems (CASs) play an increasingly important role in pure mathematics research. These are immensely complicated pieces of software that allow the user to represent and handle abstract mathematical objects within a computer. Handing these objects requires expensive computations and involves heuristics that choose the most appropriate computational methods. 

Each heuristic attempts to predict which of the available computational methods will be most effective in the current circumstances. The choice does not affect the correctness of the solution – it is essential that the output remains correct – but affects running times. Because of the exponential nature of many of the algorithms used by a CAS, a poor choice can make the difference between a computation finishing in under a second, to requiring years to complete. 

Traditionally, such heuristics are designed by humans who study at most a few hundred examples and produce common-sense-based algorithms. They are strongly influenced by the use cases they are familiar with. It has been shown that computers usually outperform humans in such predictions, although it is often a non-trivial task to employ machine learning within computational software due to the complexity of the patterns and the huge variability in use cases. 

This project will develop machine learning tools for algorithm selection, which can be embedded in an existing CAS. These tools will replace the existing hard-coded heuristics, and a life-long-learning mechanism will ensure that they develop over time in response to real-world use cases. 

This pioneering project will place the student at the forefront of an exciting new area of research: the application of machine learning techniques to exact symbolic computations. This work has enormous potential for collaborative projects with CAS research groups worldwide: for example, the hugely successful SAGE open source project; the DFG-funded Singular and OSCAR groups in Germany; and the Magma group in Australia. 

An ideal candidate will have computer science or engineering background, with interest in mathematics and artificial intelligence. Good programming skills are essential. 

Supervisors: Dr Daniel Karapetyan  (School of Computer Science), Dr Alexander Kasprzyk (School of Mathematical Sciences). 

For further details and to arrange an interview please contact Dr Daniel Karapetyan  (School of Computer Science).

Machine learning-assisted growth of atomically thin semiconductors

Development of quantum systems and understanding of their complex behaviour - from quantum tunnelling to entanglement - have led to revolutionary discoveries in science. Quantum science has great potential, but future progress requires a shift towards transformative materials and advanced fabrication methods. This project will use a bespoke cluster (EPI2SEM) for EPitaxial growth and In-situ analysis of two dimensional semiconductors (2DSEM) to create the high-purity materials and research tools required to advance the field beyond present state-of-the art. By using computational modelling and machine learning (ML), we aim to realise an artificial intelligence semiconductor-synthesis control system. The successful growth of 2DSEM demands strict control of many conditions, including temperature, pressure, atom fluxes and their ratio, growth rate, etc. 

We will explore the thermodynamics and growth kinetics including reaction pathways, surface migration, and reaction rate of 2DSEM on specific substrate surfaces. The proposed approach of “cooking” (i.e. define recipes for the growth) and “tasting” (i.e. growth and measurement) will be applied to the fabrication of atomically thin-semiconductors with ultra-high electron mobilities for nanoelectronics. Advanced computational and ML simulations (with Prof. Elena Besley, School of Chemistry), combined with complementary experimental tests (with Prof. Amalia Patanè, School of Physics), will provide a powerful toolkit for unveiling the real-time growth mechanism. 

In this project, machine learning (ML) methods will be utilised to predict thin-film growth of semiconductor materials. The ML predictions will be tested in a unique in the world, bespoke facility EPI2SEM (School of Physics) and by our industrial partner Paragraf (Greater Cambridge Area). Synthesis-by-design (with Prof. Amalia Patanè, School of Physics) guided by computation (with Prof. Elena Besley, School of Chemistry) will reduce the need for cost- and time- consuming trial-and-error experiments. 

Applicants will be expected to have a numerate background from a first degree in Maths, Chemistry, Physics or similar, strong interest in applied machine learning and developing new coding and data science skills. 

Supervisors: Prof Elena Besley (School of Chemistry),  Prof Amalia Patanè (School of Physics). 

For further details and to arrange an interview please contact Prof Amalia Patanè (School of Physics).

Multimodal machine learning of parent-child interactions as a predictor of child cognitive functions

The first five years of the child’s life play a critical role for cementing cognitive functions. Several studies have shown that the quantity and quality of parent-child interactions can affect children’s cognitive development later on. Periods of shared attention between caregivers and children have important implications for developing the child’s attention span and language skills. Recent neuroimaging research has also found that some parts of the brain may be activated in similar ways for both parents and children during these interactions, and interestingly, the extent of this analogous brain activation might be influenced by factors like how stressful the home environment is. Despite what is known about the association between child cognitive functions and the modalities (i.e., audio-visual and brain activity) involved in parent-child interactions, the extent to which these can be combined to inform better cognitive developmental outcomes for infants is unknown. If we can better understand these associations, we can help caregivers interact with young children in more effective ways that could potentially transform their development in the crucial early stages of life. 

In this project you will work to better understand the interactions between young children and caregivers as they interact during exploratory play. You will apply state-of-the art machine learning techniques to analyse videos of interactions, detecting poses, activities and key events. You will explore novel deep learning methods for integrating multi-modal information sources, to combine video events, audio data and FNIRS data and extract perceptual, verbalization, affect, brain function information (to name a few) during parent-child interactions to predict cognitive functions in children. Video, questionnaire and neuroimaging data are already available from an ongoing, longitudinal project assessing neurocognition in children in the School of Psychology at the University of Nottingham. Separately, it might be possible to design and collect more data in the future. 

Applicants will be expected to have a good working experience with current machine learning and image processing tools and techniques. Prior knowledge of biomedical signal processing and natural language processing is desirable but not essential. 

Supervisors: Dr Joy Egede (School of Computer Science), Dr Sobana Wijeakumar , (School of Psychology), Dr Aly Magassouba (School of Computer Science)  [email protected] . 

For further details and to arrange an interview please contact Dr Joy Egede (School of Computer Science)

Physics-informed Machine Learning for Climate Wind Change

Machine learning simulation strategies for fluid flows have been extensively developed in recent years. Particular attention has been paid to physics-informed deep neural networks in a statistical learning context. Such models combine measurements with physical properties to improve the reconstruction quality, especially when there are not enough velocity measurements. In this project, we will develop novel methods to reconstruct and predict the velocity field of incompressible flows given a finite set of measurements. Specifically, using wind data from the Met Office, we aim at reconstructing the wind in the UK for the last 50 years and predict the main features of the wind in the UK in the upcoming decades and compare it against climate change models (CMIP6 and ERA5) based on classical data assimilation. For the spatiotemporal approximation, we will further develop the Physics-informed Spectral Learning (PiSL) framework [1,2,3] which has controllable accuracy. Our computational framework thus combines supervised (wind data) and unsupervised (physical conservation laws) learning techniques. From a mathematical standpoint, we will study the stability and robustness of the method whereas, from a computer science standpoint, we will develop efficient algorithms for the adaptive construction of the sparse spectral approximation. 

We are looking for master's degree holders who are interested in interdisciplinary research projects that revolve around computational methods such as mathematical models, simulation methods, and data science techniques. Applicants are expected to have a numerate background from a first degree in Maths, Physics, Computer Science or Engineering; and an interest in developing novel physics-informed machine learning approaches and in developing their coding and data science skills. It is essential that the applicant is proficient in using either Python or similar high-level programming languages. 

[1] Espath, L., Kabanov, D., Kiessling, J. and Tempone, R., 2021. Statistical learning for fluid flows: Sparse Fourier divergence-free approximations. Physics of Fluids, 33(9), p.097108.

[2] Kabanov, D.I., Espath, L., Kiessling, J. and Tempone, R.F., 2021. Estimating divergence‐free flows via neural networks. PAMM, 21(1), p.e202100173.

[3] Saidaoui, H., Espath, L. and Tempone, R., 2022. Deep NURBS–admissible neural networks. arXiv preprint arXiv:2210.13900.

Supervisors: Dr Luis Espath (School of Mathematical Sciences), Dr Xin Chen (School of Computer Science).  

For further details and to arrange an interview please contact Dr Luis Espath (School of Mathematical Sciences). 

Uncertainty quantification for machine learning models of chemical reactivity

In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. 

Predicting chemical reactivity is, in general, a challenging problem and one for which there is relatively little data, because experimental chemistry takes time and is expensive. Within our research group, we have a highly automated workflow for high-level quantum chemical calculations and we have generated thousands of examples relating to the reactivity of molecules for a specific chemical reaction. This project will evaluate a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. 

The algorithms developed in the project will be implemented in our ai4green electronic lab notebook, which is available as a web-based application: http://ai4green.app and which is the focus of a major ongoing project supported by the Royal Academy of Engineering. The results of the project will help chemists to make molecules in a greener and more sustainable fashion, by identifying routes with fewer steps or routes involving more benign reagents. 

Applicants should have, or expected to achieve, at least a 2:1 Honours degree (or equivalent if from other countries) in Chemistry or Mathematics or a related subject. A MChem/MSc-4-year integrated Masters, a BSc + MSc or a BSc with substantial research experience will be highly advantageous. Experience in computer programming will be essential. 

Supervisors: Prof Jonathan Hirst (School of Chemistry), Prof Simon Preston (School of Mathematical Sciences).

For further details and to arrange an interview please contact Prof Jonathan Hirst (School of Chemistry).

Using AI to create ‘super-glasses’ to see cells deep inside living tissue for biomedicine

Imaging with cellular resolution (~1 micron) is a critically important tool for understanding biological processes that underpin diseases from Alzheimer’s to cancer. Images are usually captured with light (e.g. with microscopes), but this can penetrate less than 1mm into tissue. This limitation prevents imaging deep inside large organs of the body, such as the brain, without greatly reducing resolution. The main reasons for the limited penetration depth of light into tissue is aberrations and scattering, which cause blurry images, akin to what a glasses-wearer might see without corrective lenses, or diffuse images, akin to trying to see through fog. 

Recent work has shown that special ‘glasses’, in the form of adaptive optical systems, can be created for microscopes that reverse the distortion caused by aberrations and scattering to produce clear images. Scattering and aberrations are a result of the light travelling through the inhomogeneous tissue, the properties of which vary in spatial but also in time for living samples. To become a truly transformative technology these glasses must in future be able to correct in real-time to account for the sample, and hence the image distortions, changing with time. To do this, they must incorporate real-time measurements that help with estimating changes to the sample. In other words, they must be upgraded to ‘super-glasses’. Such technology has only recently become feasible due to advances in generative image AI. 

In this Ph.D. you will apply and extend one of the most successful generative image reconstruction techniques, Diffusion models, to correct for scattering and aberrations in two important real-world case studies: imaging deep in biological tissue models (e.g. for personalized medicine), and imaging through ultra-thin optical fibers to make medical endoscopes for disease diagnosis. You will implement an autoregressive learning model to incorporate recent measurement data into your diffusion models via a conditioning approach. Working with collaborators in the lab you will also explore options to control microscope wavefront shaping using reinforcement learning. The final system will maintain a real-time physical approximation of the sample state in a dynamically changing environment. 

Applicants are expected to have a strong background in computing and programming, and some knowledge of machine learning and deep learning would be idea. Any knowledge of the relevant physics (optics/electromagnetism) would be useful but it is not essential and can be learned on the job. The candidate will be supported in the different areas of knowledge required by a multidisciplinary team from Computer Science and Engineering, and will work with datasets measured in-house. 

Supervisors: Dr Michael Pound (School of Computer Science), Prof Amanda Wright (Faculty of Engineering), Dr George Gordon (Faculty of Engineering).

For further details and to arrange an interview please contact Dr Michael Pound (School of Computer Science).

  Further information For further enquiries, please contact  Professor Ender Özcan  - School of Computer Science

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PhD in Ethics of Artificial Intelligence and Big Data

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Tech Secretary unveils £8.5 million research funding set to break new grounds in AI safety testing

Up to £8.5 million of government research grants will be offered to improve society’s resilience to the risks arising from the development of new AI technologies.

£8.5 million grants programme to fund research into systemic AI safety.

• New grants offered to researchers to push boundaries of AI safety research
• funding programme launched as UK government seeks to explore new methods to increase safe and trustworthy deployment of AI  Funded research
• grants will aim to understand how society can adapt to the transformations generated by the advent of AI .

At the AI Seoul Summit today (22 May), which is co-hosted by the UK and Republic of Korea, Technology Secretary Michelle Donelan announced that the UK government will offer grants to researchers to study how to protect society from AI risks such as deepfakes and cyberattacks, as well as helping to harness its benefits, such as increased productivity. 

The most promising proposals will be developed into longer-term projects and could receive further funding.

The programme (published on www.aisi.gov.uk) will be led within the UK government’s pioneering AI Safety Institute by Shahar Avin, an AI safety researcher who will be joining the UK’s Institute on secondment and Christopher Summerfield, UK AI Safety Institute Research Director. The research programme will be delivered in partnership with UK Research and Innovation and The Alan Turing Institute and the UK AI Safety Institute will aim to collaborate with other AI Safety Institutes internationally. Applicants will need to be based in the UK but will be encouraged to collaborate with other researchers from around the world. 

The UK government’s pioneering AI Safety Institute is leading the world in the testing and evaluation of AI models, advancing the cause of safe and trustworthy AI . Earlier this week, the AI Safety Institute released its first set of public results from tests of AI models. It also announced a new office in the US and a partnership with the Canadian AI Safety Institute – building on a landmark agreement with the US earlier this year.  

The new grants programme is designed to broaden the Institute’s remit to include the emerging field of ‘systemic AI safety’, which aims to understand how to mitigate the impacts of AI at a societal level and study how our institutions, systems and infrastructure can adapt to the transformations this technology has brought about.   

Examples of proposals within scope would include ideas on how to curb the spread of fake images and misinformation by intervening on the platforms that spread them, rather than on the AI models that generate them.

Technology Secretary Michelle Donelan, said:  

When the UK launched the world’s first  AI Safety Institute last year, we committed to achieving an ambitious yet urgent mission to reap the positive benefits of AI by advancing the cause of AI safety.   With evaluation systems for AI models now in place, Phase 2 of my plan to safely harness the opportunities of AI needs to be about making AI safe across the whole of society.  This is exactly what we are making possible with this funding which will allow our Institute to partner with academia and industry to ensure we continue to be proactive in developing new approaches that can help us ensure AI continues to be a transformative force for good.  I am acutely aware that we can only achieve this momentous challenge by tapping into a broad and diverse pool of talent and disciplines, and forging ahead with new approaches that push the limit of existing knowledge and methodologies.

The Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry, said:

Canada continues to play a leading role on the global governance and responsible use of AI . From our role championing the creation of the Global Partnership on AI ( GPAI ), to pioneering a national AI strategy, to being among the first to propose a legislative framework to regulate AI , we will continue engaging with the global community to shape the international discourse to build trust around this transformational technology.

The AISI Systemic Safety programme aims to attract proposals from a broad range of researchers across both the public and private sectors, who will work closely with the UK government to ensure their ideas have maximum impact. 

It will run alongside the Institute’s evaluation and testing of AI Models, where the Institute will continue to work with AI labs to set standards for development and help steer AI towards having positive impact.

Christopher Summerfield, UK AI Safety Institute Research Director, said:   

This new programme of grants is a major step towards ensuring that AI is deployed safely into society. We need to think carefully about how to adapt our infrastructure and systems for a new world in which AI is embedded in everything we do. This programme is designed to generate a huge body of ideas for how to tackle this problem, and to help make sure great ideas can be put into practice.

The AI Seoul Summit builds on the inaugural AI Safety Summit hosted by the United Kingdom at Bletchley Park in November last year and is one the largest ever gathering of nations, companies and civil society on AI .

UKRI Chief Executive, Professor Dame Ottoline Leyser said: 

The AI Safety Institute’s work is vital for understanding AI risks and creating solutions to maximise the societal and economic value of AI for all citizens. UKRI is delighted to be working closely with the Institute on this new programme to ensure that institutions, systems and infrastructures across the UK can benefit safely from AI .    This programme leverages the UK’s world-leading AI expertise, and UKRI ’s AI investment portfolio encompassing skills, research, infrastructure and innovation, to ensure effective governance of AI deployment across society and the economy.    The programme will bring safety research right into the heart of government, underpinning the pro-innovation regulation that will shape the UKs digital future.

Professor Helen Margetts, director of public policy at The Alan Turing Institute, said:

We’re delighted to be part of this important initiative which we hope will have a significant impact on the UK’s ability to tackle threats from AI technology and keep people safe. Rapidly advancing technology is bringing profound changes to the information environment, shaping our social, economic and democratic interactions.  That is why funding AI safety is vital – to ensure we are all protected from the potential risks of misuse while maximising the benefits of AI for a positive impact on society.

Notes to editors

AI researcher Shahar Avin will lead the grants programme from within the UK AI Safety Institute, and bring a wealth of knowledge and experience to ensure proposals reach their fullest potential in protecting the public from risks of AI while harnessing its benefits. He is a senior researcher associate at the Centre for the Study of Existential Risk ( CSER ) and previously worked at Google. 

The programme will be delivered in partnership with UK Research and Innovation and The Alan Turing Institute.

You can read more about the recent announcement on the Institute’s office opening in San Francisco, the AI models testing result, and UK AISI ’s partnership with the US and Canadian AI Safety Institutes .

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UK opens office in San Francisco to tackle AI risk

Ahead of the AI safety summit kicking off in Seoul, South Korea later this week , its co-host, the United Kingdom, is expanding its own efforts in the field. The AI Safety Institute, a U.K. body set up in November 2023 with the ambitious goal of assessing and addressing risks in AI platforms, has said it will open a second location in San Francisco. 

The idea is to get closer to the epicenter of AI development. The Bay Area is the home of companies like OpenAI, Anthropic, Google and Meta that are building foundational AI technology.

Foundational models are the building blocks of generative AI services and other applications, and it’s interesting that although the U.K. has signed an MOU with the U.S. to collaborate on AI safety initiatives, the U.K. is still choosing to set up in the U.S. to tackle the issue.

“By having people on the ground in San Francisco, it will give them access to the headquarters of many of these AI companies,” Michelle Donelan, the U.K. secretary of state for science, innovation and technology, said in an interview with TechCrunch. “A number of them have bases here in the United Kingdom, but we think that would be very useful to have a base there as well, and access to an additional pool of talent, and be able to work even more collaboratively and hand-in-glove with the United States.”

Part of the reason is that being closer to that epicenter is useful not just for understanding what is being built, it also gives the U.K. more visibility with these firms. That’s important, since AI and technology is seen by the U.K. as a huge opportunity for economic growth and investment. 

And given the latest drama at OpenAI around its Superalignment team , it feels like an especially timely moment to establish a presence there.

The AI Safety Institute, launched in November 2023, is a relatively modest affair today. The organization has just 32 employees, a veritable David to the Goliath of AI tech, when you consider the billions of dollars of investment riding on the companies building AI models and their own economic motivations for getting their technologies into the hands of paying users. 

One of the AI Safety Institute’s most notable developments was the release of Inspect , its first set of tools for testing the safety of foundational AI models, earlier this month. 

Donelan today referred to that release as a “phase one” effort. Not only has it proven challenging to benchmark models, but for now, engagement is very much an opt-in and inconsistent arrangement. As one senior source at a U.K. regulator pointed out, companies are under no legal obligation to have their models vetted at this point; and not every company is willing to have their models vetted before release. That could mean, in cases where risk might be identified, the horse may have already bolted. 

Donelan said the AI Safety Institute was still working on strategies to engage with AI companies to evaluate them. “Our evaluation process is an emerging science in itself,” she said. “So with every evaluation, we will develop the process and finesse it even more.”

Donelan said that one goal of the conference in Seoul is to present Inspect to regulators, aiming to get them to adopt it, too. 

“Now we have an evaluation system. Phase two needs to also be about making AI safe across the whole of society,” she said. 

Longer term, Donelan believes the U.K. will be building more AI legislation, although, repeating what Prime Minister Rishi Sunak has said on the topic, it will resist doing so until it better understands the scope of AI risks. 

“We do not believe in legislating before we properly have a grip and full understanding,” she said, noting that the institute’s recent international AI safety report, focused primarily on trying to get a comprehensive picture of research to date, “highlighted that there are big gaps missing and that we need to incentivize and encourage more research globally.”

“Also, legislation takes about a year in the United Kingdom. If we had just started legislation when we started instead of [organizing] the AI Safety Summit [held in November last year], we’d still be legislating now, and we wouldn’t actually have anything to show for that,” Donelan said.

“Since day one of the Institute, we have been clear on the importance of taking an international approach to AI safety, share research, and work collaboratively with other countries to test models and anticipate risks of frontier AI,” said Ian Hogarth, chair of the AI Safety Institute, in a statement. “Today marks a pivotal moment that allows us to further advance this agenda, and we are proud to be scaling our operations in an area bursting with tech talent, adding to the incredible expertise that our staff in London has brought since the very beginning.”

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Phil Muncaster

UK / EMEA News Reporter , Infosecurity Magazine

• Follow @philmuncaster

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Announced by technology secretary Michelle Donelan at the AI Seoul Summit today, the research grants will focus on “systemic AI safety” – that is, understanding how better to protect society from AI risks and harness the technology’s benefits.

The research program will be led by researcher Shahar Avin at the government’s AI Safety Institute and delivered in partnership with UK Research and Innovation and The Alan Turing Institute. Although applicants will need to be based in the UK, they will be encouraged to collaborate with other researchers in AI safety institutes around the world.

AI represents a two-pronged threat to economic and social stability. On the one hand, AI systems could be targeted by techniques such as prompt injection and data poisoning, and on the other, the technology can be used by threat actors themselves to gain an advantage.

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Read more on AI threats: UK Financial Regulator Urges Banks to Tackle AI-Based Fraud

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At the same time, three-quarters (76%) of respondents claimed that AI technology is improving information security and 64% said they are increasing their budgets accordingly over the coming year.

AI Safety Institute research director, Christopher Summerfield, claimed the new funding represents a “major step” to ensuring AI is deployed safely in society.

“We need to think carefully about how to adapt our infrastructure and systems for a new world in which AI is embedded in everything we do,” he added . “This program is designed to generate a huge body of ideas for how to tackle this problem, and to help make sure great ideas can be put into practice.”

The institute has already been conducting valuable research into AI threats. A May update published on Monday  revealed that four of the most used generative AI chatbots are vulnerable to basic jailbreak attempts.

The UK and South Korea yesterday hailed  a “historic first” as 16 major AI companies signed new commitments to safely develop AI models.

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