phd machine learning spain

PhD in Artificial Intelligence

IIIA Institut d'Investigació en Intel·ligència Artificial

Artificial Intelligence Research Institute

Annual report 2020  ☰   ☷.

Research Groups ➲

Learning from data & experience

Machine Learning

Interactions in socio-technical systems

Multiagent Systems

Representation & reasoning, optimisation, and swarm intelligence

Logic & Reasoning

Research themes  ➲.

Ethics and AI

AI and Education

AI for Healthcare

A sample of our ongoing projects  ➲.

AI for inclusion & diversity

Play & Sing

Playing & singing for brain recovery.

Optimising the European Supply Chain

Open positions  ➲, p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } software engineer - scetria software engineer - scetria, on  p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } 11/apr/2024 11/apr/2024, p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } join our team: open position in the allies postdoctoral program join our team: open position in the allies postdoctoral program, on  p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } 21/mar/2024 21/mar/2024, p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } join our team: open position in the allies postdoctoral program join our team: open position in the allies postdoctoral program, on  p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } 01/mar/2024 01/mar/2024, impact areas.

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Sustainable development goals  ➲.

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 03: Good Health and Well-being GOAL 03: Good Health and Well-being

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 04: Quality Education GOAL 04: Quality Education

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 06: Clean Water and Sanitation GOAL 06: Clean Water and Sanitation

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 07: Affordable and Clean Energy GOAL 07: Affordable and Clean Energy

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 09: Industry, Innovation & Infrastructure GOAL 09: Industry, Innovation & Infrastructure

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 10: Reduced Inequality GOAL 10: Reduced Inequality

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 11: Sustainable Cities and Communities GOAL 11: Sustainable Cities and Communities

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 13: Climate Action GOAL 13: Climate Action

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } GOAL 16: Peace, Justice and Strong Institutions GOAL 16: Peace, Justice and Strong Institutions

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Industry engagement  ➲.

Collaborate with us

Tech. Transfer & Development Unit

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p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } Atilim Atilim   p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } Günes Baydin Günes Baydin

I had a great experience at IIIA. It is one of the best places to get involved in AI research and start a career that can lead to many opportunities in academia and industry.

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } Arnau Arnau   p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } Ramisa Ramisa

I have very fond memories of my PhD at the IIIA. I learned a lot and made great friends in a very supporting environment. The "costellades" at Mas Puig and the Christmas concerts were incredible!

p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } Tomas Tomas   p { vertical-align: top; } .field-value { display: inline; } .hide_element { display: none; visibility: hidden; background-color: red; width: 0px; } .show_element { display: inline; visibility: visible; } Trescak Trescak

Life-changing experience, that opened the world to me. Went from boring corporate environment to the world where almost anything is possible. Worked with the  best researchers in the field and enjoyed every second of it.

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CSIC's network of research groups dedicated to basic research in Artificial Intelligence.

The Catalan Government's alliance bringing together research groups in Artificial Intelligence.

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CRG INTERNATIONAL PHD FELLOWSHIPS PROGRAMME

Apply for a PhD @ CRG

 |  Eligibility criteria  |  How to apply   |   Selection process  | CRG PhD call timeline |  Fellowships Available | Workshops | Contact

Apply now to boost your scientific career at the Centre for Genomic Regulation (CRG) in Barcelona. The CRG is a center of excellence with international teams representing a broad range of disciplines, with first-class core technologies to support the research projects, a wide range of seminars given by high-profile invited speakers, and courses on complementary and transferable skills integrated with the training programme .

Scroll down to learn more about our 17 fully PhD fellowships

On the application form, you will be able to indicate up to two opportunities for which you wish to be considered.

START YOUR APPLICATION

Are you ready to prepare an outstanding application?

Join us online for tips and to get answers to your questions. Friday, 15th December 2023 3pm CET

REGISTER NOW

In this call, we offer 17 fully funded PhD fellowships in the following CRG labs:

AL JORD LAB

Biomolecular Condensates | Cytoskeletal & Nuclear Dynamics | RNA Processing | Advanced (Live) Microscopy | Spatial Transcriptomics | Mechanobiology

Our Research

The “Mechanics of Organelle Remodeling” group’s interdisciplinary research aims to decipher how cytoskeletal forces impact nuclear dynamics across scales in health & disease, with a focus on the mechano-regulation of nuclear biomolecular condensates & RNA-processing.

Who are we looking for?

We are looking for aspiring scientists who are curious, creative, and driven by cross-disciplinary research in biology. Trained in Cell Biology, Molecular Biology &/or Biophysics , the candidate must also have solid interests in multiplexing , (live) imaging, spatial transcriptomics, & mechanobiology.

MARTINEZ LAB

Gene Regulation | Signal Transduction | Non-Linearities | Statistical Mechanics | Deep Learning

The group is interested in general and fundamental principles governing signal transduction and gene regulation in eukaryotic cells, which we uncover using a combination of mechanistic mathematical modelling and experimental data analysis from the literature or collaborators. We are based at the Barcelona Collaboratorium.

A researcher with a quantitative background (physics, mathematics, engineering or related areas) who is fascinated by how collections of molecules lead to living cells and organisms. Some experience programming and modelling , preferably living systems, is required.

SEBE-PEDROS Lab

Comparative Transcriptomics | Genome Evolution | Population genomics | Cis-Regulatory Evolution | Phylogenetics

We study the origin and evolution of cell type programs and associated genome regulation mechanisms: transcription factors, 3D chromatin architecture, histone modifications, and more. To this end, we combine single-cell genomics, chromatin profiling, and phylogenetic comparative methods in diverse animals and unicellular eukaryotes.

We are looking for candidates broadly interested in evolutionary biology, gene regulation, and comparative genomics ; and with a background in genetics, functional genomics and/or computational biology.

Computational Modelling | Evolutionary processes | Fitness Landscapes | Genotype-Phenotype Maps | Molecular Structures

Nora Martin’s group at the Collaboratorium is building computational and theoretical models to gain a better quantitative understanding of variation, for example, mutational changes in molecular structures, and investigate the interplay between variation and selection in evolutionary processes.

A highly motivated researcher with a background in physics , mathematics (or another discipline with a strong emphasis on theory and computational modelling ) to apply their skills to the fascinating field of evolutionary processes across biological scales.

RODRIGUEZ LAB

Repetitive DNA | Genomic Variation | Functional genomics | Population Genetics | Cancer Genomics | Computational Biology

Our team investigates how diverse repetitive DNA classes shape the genome of humans and other species, acting as a source of genetic variation and functional elements. We address this question using multiomic sequencing technologies, computational methods development and application to large datasets.

An enthusiastic, proactive and eager to learn researcher interested into developing a PhD on computational biology. The ideal candidate should have knowledge on how genomes operate , besides programming skills, including Python and Bash scripting.

BEEKMAN LAB

Lymphoma | Epigenetics | 3D Chromatin Structure | Translocations | DNA Methylation

Our lab focused on the understanding of normal B-cell differentiation and lymphoma formation from the perspective of 3D chromatin structure, DNA methylation and heterogeneity in healthy and pre-malignant cell populations.

We are looking for an enthusiastic candidate with a wetlab background of at least 1 year in the field of molecular biology and epigenetics with the interest to pursue a wetlab-oriented project.

Transcription Regulation | Syntactic and Semantic Patterns in the Genome Sequence | Computational Biology | Bioinformatics | Statistics | Evolution Gene Expression

Our lab is interested in understanding the genetic and epigenetic factors that regulate transcription, and the relationship between transcriptomes and phenotypes. The lab is also interested in the understanding of the syntactic and semantic patterns in the genome sequence that define genes, and in developing methods to predict genes in the genomes of eukaryotic species.

We are looking for a highly motivated researcher with a background in computational biology and bioinformatics . Familiarity with basic evolutionary and molecular biology concepts , and good statistical (data analysis) and programming skills are a must. Familiarity with mathematical and quantitative approaches is preferable.

WEGHORN LAB

Cancer Genomics | Human Evolution | Probabilistic Models of Evolution | Selection Inference | Mutational Processes

The "Evolutionary Processes Modeling" group, led by Dr Donate Weghorn, is interested in the quantitative modeling of mutational processes in DNA sequences of both cancer tumors as well as the human germline. We use these mutation probabilities to identify genomic loci that cause cancer or human diseases by comparing the observed mutations to models of neutral evolution.

We are looking for a highly motivated candidate with an educational background in physics, mathematics, statistics or other quantitative sciences and a strong interest in biology and evolution .

STROUSTRUP LAB

Aging | Systems Biology | Molecular Genetics | Computational Biology

Our group studies the molecular origins of aging, working to understand how functional declines at the molecular level can lead to emergent aging phenotypes at the organismal-level. We ask questions like: why do some individuals live longer than other?  Why do point mutations in the insulin receptor double lifespan?  We are an interdisciplinary group, combining experimental methods taken from functional genetics, systems biology, and CRISPR genome editing with a variety of statistical and phenomenological modelling techniques.

We are looking for two types of candidates 1) wet-lab researchers interested in studying the molecular genetics of aging using a variety of quantitative techniques 2) computational biologists interested in applying bioinformatics tools to ask questions inspired by statistical physics.

Stem Cell | Regeneration | Retina | Chromatin | Super Resolution Imaging | Computational Science

The main interests of our group are to dissect mechanisms and factors controlling cell fate transition and tissue regeneration from the single cell to the whole organ level. We use a variety of techniques including super-resolution microscopy to study the spatial chromatin arrangement in somatic and stem cells.

We are looking for students excited to discover fundamental and curiosity-driven questions within the area of interest of our group.

Single Cell Genomics | Deep Learning | Bioinformatics | Stem Cells | Gene Regulation | Ageing

The Velten lab uses single-cell and synthetic genomics, high-throughput genetic screens and artificial intelligence to study the genomic regulation of differentiation programs in hematopoietic and leukemic stem cells. Currently, important topics in the lab are clonal dynamics in blood during ageing, as well as the genomic encoding of differentiation programs.

We are looking for a motivated PhD student in bioinformatics with an interest in single cell genomics and/or deep learning approaches . Prior experience in bioinformatics is required.

Cytoskeleton | Microtubules | Cell Division | Cancer | Aneuploidy

The Vernos Group aims to understand how the bipolar spindle segregates faithfully the chromosomes during mitosis and meiosis and the mechanisms that may be at stake in pathologies such as infertility and cancer.

We are looking for a highly motivated researcher with a background in cell and cancer biology and solid skills in quantitative cell biology and advanced optical microscopy methods .

Cytoskeleton | Self-organization | Molecular crowding | Phase separation | Microfabrication | Fluorescence Microscopy | Molecular Motors

The 'Intracellular Self-Organization Group' headed by Dr Thomas Surrey aims to understand from a biochemical and physical perspective how human cells organize their dynamic intracellular architecture, focusing on the microtubule cytoskeleton.

A highly motivated researcher who is trained in biochemistry and/or biophysics . The ideal candidate has a strong interest in understanding biological questions at a mechanistic level and enjoys developing new experimental approaches.

Oocyte | Longevity | Metabolism | Proteostasis | Fertility

Poor oocyte quality accounts for the majority of female fertility problems, however, we know little about how oocytes can remain healthy for many years or why their health eventually declines with advanced age. World-wide data show that more than 25% of female fertility problems are unexplained, pointing to a huge gap in our understanding of female reproduction. Our lab strives to help fill this gap by studying immature oocytes.

If you are enthusiastic to discover the strategies that en able a cell to evade ageing f or decades, join us! Experience with animal handling is a plus.

IRIMIA LAB*

Single Cell RNA-seq | Comparative Transcriptomics | Gene Regulatory Networks | Alternative Splicing | Evolutionary Medical Genomics | Diabetes

The "Transcriptomics of vertebrate development and evolution" group, headed by Dr Manuel Irimia, investigates how changes in transcriptomes can lead to evolutionary innovations as well as to disease states and susceptibilities.

We are looking for a candidate interested in applying evolutionary approaches and concepts to understand human biology and disease . Previous experience in transcriptomic analyses and bioinformatics will be highly valuable. * M. Irimia has a double affiliation UPF/CRG

Gut Microbiome | Horizontal Gene Transfer | Microbiome Transmission | Metagenomics | Evolutionary Medical Genomics

A key interest of our group is to understand host-microbiome interactions. In particular, we study the factors that influence the gut microbiome, including genetic effects from the host and microbiome exchanges between socially interacting hosts.

We are looking for a proactive candidate with a computational background (python and/or R ). Prior knowledge in ( meta)genomic data analysis is desirable but not compulsory if you are eager to learn.

This position will be cosupervised by Mireia Valls-Colomer tenure-track group leader at Pompeu Fabra University (UPF).

DIAS & FRAZER LAB

Evolutionary Medicine | Deep Learning | Rare Disease | Common Disease | Conservation Genomics | Bayesian Statistics | Generative Modelling | Variant Effect Prediction

The Probabilistic Machine Learning and Genomics Group, co-led by Mafalda Dias and Jonathan Frazer, develops models of the effect of genetic variation on the health of humans and other species. The primary goal is to develop tools which can aid in diagnosis, preventative care, and discovery of the genetic underpinnings of disease. We study the full spectrum of rare and common diseases and build models ranging in scale from single proteins to whole genomes. We are a 100% computational group using techniques from deep learning, Bayesian statistics, comparative genomics and population genetics.

We are looking for someone who loves model building and is keen on studying disease from an evolutionary perspective . This position is part of a joint project in collaboration with the Transcriptomics of Vertebrate Development and Evolution Group, headed by Manuel Irimia. Experience in statistics, deep learning, or transcriptomics is desirable but not essential.

Eligibility criteria

Candidates should fulfill the following eligibility criteria at the time of the call deadline. If it becomes clear before, during, or after the evaluation phase that one or more of the eligibility criteria have not been fulfilled, the proposal will be declared ineligible and withdrawn from further examination.

• The call is open to candidates of any nationality.
• Candidates must have obtained a University Bachelor's Degree and a Master's Degree in biomedical sciences within the European Higher Education System ( minimum 300 ECTS) or an equivalent University Degree that allows to start a PhD thesis in Spain. Candidates who expect to be awarded with such a degree by September 2024 are eligible to apply.
• Candidates must have an excellent academic record , previous research experience, and a strong commitment to scientific research.
• Candidates must have a high working knowledge of English.
• Candidates invited to previous CRG PhD call interviews are not eligible.
• Candidates performing their Masters at the CRG are not eligible.
• Candidates may not have worked at the CRG for more than 3 months before the call deadline.

How to apply

• Applications must be submitted online - no applications by email will be considered. 
• Candidates must register in order to use the online application system.
• The online application form requests all of the necessary information for the initial stages of the selection process ( General information, education, academic transcripts, scholarships, prizes and awards, research experience, scientific interests and reference letters ).
• If the academic transcripts are not in Catalan, Spanish or English applicants should also attach a translation in one of the above-mentioned languages.
• Two reference letters will be automatically requested from the referees proposed by the candidate through the online system.
• Applicants should select a maximum of 2 CRG labs  in which they would like to start a PhD.
• Candidates must ensure that all information and documents are included before the deadline , including the reference letters. Incomplete proposals will not be considered.

Once the application is submitted, an acknowledgment of receipt will be automatically sent by e-mail to the applicant.

For any additional information, please check the FAQs.  

Selection process

The selection of the fellows will be based on the candidates' academic qualifications and research excellence.

• Pre-selection : The pre-selection will be based on the candidate's application, reference letters, research interests, motivation, and fit to the selected labs. If the candidate is pre-selected, she/he will be invited to an online panel interview via Zoom on the 29th of January 2024 . If successfully passes the panel interview, the candidate will be invited to Barcelona for the presential interviews.
• Presential interviews : The interviews will take place from the 5th to the 7th of March 2024 at the CRG in Barcelona. Accommodation and flight tickets will be provided. Please be aware that during all those days, interviews and presentations will take place. Full details regarding the interview process will be sent to invited candidates.
• Notification to candidates : Offers will be made to the successful candidates shortly after the interview. Successful candidates would be expected to start the PhD latest by October 2024.

CRG PhD Call Timeline

Fellowships available

We are organizing two online events to discuss what to take into consideration when looking for a PhD position.  These workshops will be delivered by the Training & Academic Office (TAO). To participate in the event registration is needed.

ONLINE WORKSHOP - Envision Your Research Future – How to choose a lab for your PhD & CRG opportunities

• Friday 10th November 10am CET - REGISTRATION HERE
• Friday 15th December 3pm CET -  REGISTRATION HERE

For any additional information, please contact the CRG Training & Academic Office at: [email protected]       

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26 phd-machine-learning PhD positions in Spain

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PhD . Candidate – Machine Learning and Artificial Intelligence for Forensic Non-Human DNA Recognition and Interpretation

machine learning offer a great opportunity to effectively extract evidence that will need to be calibrated in the form of a LR for presentation in court. Some members of the network, including the Ph. D

PhD in Image analysis and Data modelling for advanced microscopy applied to biomolecular systems (Barcelona)

Localization Microscopy (SMLM), particle tracking and cryo-electron tomography (cryo-ET). The measurements will be integrated using machine learning and data modelling. Our lab combines cell engineering and

5 PhD Positions at Universidad Autonoma de Madrid, Spain.

research in Computer Vision and Machine Learning and the potential applications to Biometrics, Explainability, Security, and Media Forensics (among others)? If so, we have the perfect opportunity for you! We

Funded PhD in Securing Systems Software

, and refining methods to transform insecure code into secure code, possibly employing machine learning or AI. Candidates may delve into binary software or source code leveraging compiler toolchains

Funded PhD Candidate Position - PERCEPTUAL INFERENCE GROUP - Neural encoding of temporal expectations

in San Sebastian, Spain. The group combines tools from Machine Learning , Computational Neuroscience, and state-of-the-art Human Neuroimaging to explain how our expectations and subjective priors shape

HRS2024-127 Investigador Postgraduado No Doctor / PhD Student

machine learning , cloud computing, and computer networking . Advanced levels of programming complex soutions in Java/Rust or Python. (Desirable) Experience in research, scientific publications, and a

Funded PhD Candidate Position - PERCEPTUAL INFERENCE GROUP - Modelling inference in the brain

interdisciplinary research group at the BCBL in San Sebastian, Spain. The group combines tools from Machine Learning , Computational Neuroscience, and state-of-the-art Human Neuroimaging to explain how our

PhD position at Computer Vision Center funded by the TOUCH COFUND project

. This involves data exploration, machine learning , model development, and hypothesis testing in cognitive science. Innovative Study of Creativity and Mental Health: AI-Powered Creativity Assessment: Developing

CALL 08-2024-1 Software Networking Researcher

/ ). We are looking for a highly motivated, enthusiastic, empathic person, with passion for research and desire to learn and explore new technologies, aiming at significantly improving her or his

Predoctoral researcher to work on ERC project INTREPID-101115353

beyond. To this end, we will use a multidisciplinary approach involving advanced machine learning techniques and top-of-the-line ultra-fast processing platforms to propose an innovative solution that will

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PhD in Complex Quantum Systems for Machine Learning

IFISC (CSIC-UIB)

We look for outstanding and motivated candidates with a Master related to Physics and a background in Quantum Physics to pursue a PhD at IFISC (UIB-CSIC), Spain. IFISC (Institute for Cross-Disciplinary Physics and Complex Systems) is a joint research institute of the University of the Balearic Islands ( UIB ) and the Spanish National Research Council ( CSIC ) recognized as a "Unit of Excellence" María de Maeztu Award a nd a member of the SOMMa alliance , which brings together the cutting-edge research centers and units in Spain.

The 4-years FPI fellowship is associated with the project Complex Quantum Systems funded by Agencia Estatal de Investigación and led by Prof. Roberta Zambrini, Dr. Gian-Luca Giorgi and Dr. Gonzalo Manzano. The program aims at different topics in quantum complex systems and machine learning, including:  

• Quantum reservoir computing : study of basic concepts of quantum machine learning and in particular reservoir computing techniques and their quantum implementation. Study of numerical methods to simulate spin and boson networks and implementations for relevant models for the implementation of quantum tasks in quantum reservoir computing and other neuromorphic computing approaches.                                                                                             
• Thermodynamics of reservoir computing : Fundamental properties of (quantum) stochastic thermodynamics and methods to simulate quantum trajectories. Application to assess the energetic costs of quantum reservoir computing architectures.
• Variational quantum methods : The acquired knowledge of monitored quantum systems will be applied to other settings, including different neuromorphic computing approaches and variational quantum methods.

The successful candidate will enroll in the Ph.D. Program in Physics of the University of the Balearic Island, which has been awarded the Excellence Mention of ANECA. The Program offers academic and professional training at the highest scientific level and puts the focus on the capacity to face and solve problems and make original contributions to knowledge in various fields of physics. A research stay (at least 3 months) with another group, e.g., associated with external collaborators involved in the CoCuSy project, in Finland, France, or Germany, is planned.  

The host institute (IFISC) provides a stimulating environment for the training of young scientists, with a program of weakly seminars, group meetings, journal clubs, conference participation, visits to leading researchers, and outreach activities.

How to apply

Interested candidates should send by 10/9/2023                                                                                                                          -motivation letter -CV together with a transcript of academic records -Contact of one or two researchers willing to provide a recommendation letter  to Roberta Zambrini: [email protected]

Campus Universitat de les Illes Balears 07122 Palma, Spain, Spanien

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Data Science Postdoctoral Researcher for Machine Learning Based Disease Prediction from Large-Scale Multi-Source Data - Youth-GEMs

Job information, offer description.

We are offering an exciting postdoctoral position at the University of Barcelona, within the Artificial Intelligence in Medicine lab ( www.bcn-aim.org ) about Gene Environment interactions in Mental health trajectories of Youth.

Concretely, we offer:

• A research position in beautiful Barcelona and its Mediterranean climate.
• Research experience within a prestigious university (1 st position in Spain).
• Cutting-edge research in AI for healthcare in one of the most dynamic research groups in Europe (12 active projects including an ERC grant).
• An international research environment by joining a multi-cultural team representing all continents.
• Opportunities to collaborate with international and inter-disciplinary collaborators as part of the European projects.
• Support in career development ( e.g. grant applications, supervision of PhD students).
• Travelling opportunities to scientific events, project meetings and international stays.
• Freedom to independently conduct research and contribute with own ideas.
• Flexible working hours, with possibility to telework.
• Competitive salary equivalent to Assistant or Associate Professor at the University of Barcelona, depending on experience.
• Contract duration until the end of the projects (minimum four years), with the possibility to extend the contract as part of other grants within the lab.

The requirements for the position are:

• Machine/deep learning algorithms.
• Biomedical data science.
• Medical image analysis.
• Excellent programming skills in Python and/or C++.
• Proficient English, both oral and written.
• Enthusiasm about research and medical applications of AI.
• Aptitude to work independently, lead project deliverables and meet deadlines.
• Good team spirit and participation to the lab’s scientific life.
• Aptitude to collaborate with local and international project partners (including technical and clinical collaborators).

Furthermore, knowledge in any of the following areas are desirable (but optional):

• Probability and statistics.
• Mathematics and/or applied mathematics.
• Bias detection and correction.
• Transfer learning and domain adaptation.
• Uncertainty estimation.
• Federated learning.
• Bioinformatics and/or Biomedical informatics.
• Biomedical engineering.
• Personalised and/or precision medicine.
• Digital health.

The research projects:

You will join our AI for Exposome team, as part of ongoing projects such as Youth-GEMs and HappyMums, funded by the European Commission. In these projects, we are developing new trustworthy AI solutions for personalised medicine approach to tailor the care models in the field of mental health diseases. The project will build on a unique set of big data repositories to develop AI-based predictive models of mental illness trajectories and proof-of-concept tools on actionable targets. Should you join our team, you will collaborate with several technical and clinical partners within and outside Europe ( e.g. in the Netherlands, United Kingdom, Germany, Spain, Ireland, Estonia, Italy, Croatia, Russia, Australia).

The successful candidate will join the Artificial Intelligence in Medicine Lab ( www.bcn-aim.org ), which is an integral part of the University of Barcelona’s Faculty of Mathematics and Computer Science. It is a young and dynamic research lab, highly active in international projects, and composed of >25 enthusiastic academics, researchers, students and research managers, with expertise in data science, machine/deep learning, biomedical informatics, trustworthy AI, and health-related applications. The research group has an established track record in coordination and participation in European and international projects (> 10 million Euros over the last 5 years) in biomedical data science and medical AI ( e.g. EuCanImage, euCanSHare, EarlyCause, LongITools, HealthyCloud, RadioVal, DataTools4Heart, Youth-GEMs, HappyMums, AIMIX, AI4HF).

The institution:

The University of Barcelona (UB), founded in 1450, is one of the oldest universities in Spain. It comprises a student body of 84,370 and 4,548 research staff members. With 73 undergraduate programs, 273 graduate programs and 48 doctorate programs, UB is the largest university in Barcelona and Catalonia. The UB is ranked the first Spanish university according to several rankings (QS World University Rankings 2022, Shanghai Ranking 2022). It is particularly interested in fostering international relations and, for many years, has managed an average of 150 European projects per year. The University of Barcelona is part of the prestigious League of European Universities Research (LERU).

Gross salary per year: 42.500 €

Requirements

- Machine/deep learning

- Predictive modelling

- Multi-source data integration

- Biomedical or bio-informatics

- Excellent programming skills (e.g. in Python)

- Excellent English, both oral and written

- Ability to independently present results

- Good team spirit and participation in the scientific life of the lab

- Aptitude to work independently, to lead on project deliverables to co-supervise students, and to represent the lab at meetings and events

- Aptitude to collaborate with both technical and clinical collaborators

- Passion for applications of artificial intelligence to biomedicine

- Strong writing and speaking skills

Additional Information

- PhD in computer science, data science, mathematics, applied mathematics, physics, statistics, biomedical informatics, computer vision, medical image computing, biomedical engineering, or equivalent.

- A publication record in relevant peer-reviewed journals and/or conferences.

• Adequacy of the CV (maximum 60)

        - Specialised doctorate (40), generic doctorate (20)

• Letter of motivation (maximum 30)

• Experience in the field (maximum 10)

Minimum score to pass the selection process: 60/100

- First selection based on CV and letter.

- Interviews by a panel composed of Karim Lekadir, team members and Academics from the University of Barcelona.

Duration of the contract of employment: please indicate the duration of the contract taking into account the UB regulations (from 3 months to 4 years).

The candidate proposed for hiring must accept the job offer within 5 working days from the date of notification of the selection.

Priority will be given to people with disabilities (Law 89/2015 of June 2, reserve of quota 2% in favour of people with disabilities in companies of 50 or more people).

In case of questions, please contact: [email protected]

Work Location(s)

Where to apply.

Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2023 for a start date of August 2024 in the Machine Learning PhD program, GRE Scores are REQUIRED. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2021, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2018, will not be accepted.)

Graduate Online Application

• Early Application Deadline – November 29, 2023 (3:00 p.m. EST)
• Final Application Deadline - December 13, 2023 (3:00 p.m. EST)

• 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.

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

• MyU : For Students, Faculty, and Staff

Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980 

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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Data Science & AI

Syllabi for MS/PhD Interview & Entrance Test

The written test will have two parts:

• Theory – These will be objective questions (MCQ, Fill in the blanks, True/False etc)
• Python Coding – 2 problems that you will be required to write a code for in Basic Python

Theory Syllabus

Probability and statistics.

– Counting (permutation and combinations) – independent events, mutually exclusive events – marginal, conditional and joint probability – Bayes Theorem – conditional expectation and variance – mean, median, mode and standard deviation – correlation, and covariance – random variables, discrete random variables and probability mass functions – uniform, Bernoulli, binomial distribution – Continuous random variables and probability – distribution function, cumulative distribution function, Conditional PDF – uniform, exponential, Poisson, normal, standard normal, t-distribution – chi-squared distributions – Central limit theorem – confidence interval – z-test, t-test,chi-squared test.

Linear Algebra

– Vector space, subspaces – linear dependence and independence of vectors – matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix – quadratic forms – systems of linear equations and solutions – Gaussian elimination – eigenvalues and eigenvectors – determinant, rank, nullity – projections – LU decomposition, singular value decomposition.

Calculus and Optimization

– Functions of a single variable – limit, continuity and differentiability – Taylor series – maxima and minima – optimization involving a single variable.

Programming, Data Structures and Algorithms

– Programming in Python – Basic data structures: stacks, queues, linked lists, trees, hash tables – Search algorithms: linear search and binary search – Basic sorting algorithms: selection sort, bubble sort and insertion sort – Divide and conquer: mergesort, quicksort – Introduction to graph theory – Basic graph algorithms: traversals and shortest path

Coding Syllabus

You will be given some coding tasks that you need to complete and execute by writing Python scripts. To be able to do this you will need to know the following:

– Basic Python syntax – comments, variables, basic data types – Operators and Control Flow – If/else, for, while, range, break, continue, pass = Functions – How to define and use them – Lists/Arrays, Tuples, and associated methods

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Interview Topics

For those who qualify after the written test for the online interview, questions from the following additional topics may be asked during the interview

For MS/PhD Interviews

Machine learning.

– Supervised Learning regression and classification problems – Simple linear regression – Multiple linear regression – Ridge regression – Logistic regression – k-nearest neighbour – Naive Bayes classifier – Linear discriminant analysis – Support vector machine – Decision trees – Bias-variance trade-off – Cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network – Unsupervised Learning: clustering algorithms

Artificial Intelligence (AI)

– Search: informed, uninformed, adversarial – Logic: Propositional Logic, Predicate Logic – Reasoning under Uncertainty Topics – Conditional Independence Representation – Exact Inference through Variable Elimination – Approximate Inference through Sampling

PhD applicants may also be asked questions from specialized topics for the interview – They can select a topic from Deep Learning, NLP, Vision, RL, Time-Series modeling depending on their interest and background.

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