phd in computer science finland

Doctoral Programme in Computer Science

The general theme of the DoCS doctoral programme is computer science. The focal areas of DoCS are Algorithmic bioinformatics; Algorithms, data analytics, and machine learning; Data Science; Networks and services; and Software systems.

Want to know more? Visit our profile & activities page to learn more about the programme.

A balance between studies and nature: A Medical Physics journey at UEF

The first yufe4postdocs researchers started at the university of eastern finland, alina solomon appointed as professor of neuroepidemiology, changes in pharmacy systems in the nordic countries – evidence based or an ideological playground, sustainability and circular economy morning coffee session, lisa gohlke, m.soc.sc: doctoral defence in human geography, joensuu.

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School of Computing

School of Computing that is situated on both Joensuu and Kuopio campuses provides high-quality education in the areas of computer science, information technology and statistics. Internationality is part of everyday life at the School. We engage in meaningful and international co-operation aimed at developing the education offered at the School to meet the future needs of society and working life.

Our internationally acclaimed research focuses on artificial intelligence (AI), data science, and future technologies. AI and data mining have long been utilised in many fields, and data use and analysis are set to increase in the future.

The School of Computing currently offers an opportunity to complete an international double degree at the Master’s level. The opportunity of an international double degree is provided for doctoral studies as well. We also participate in the university’s Institute of Photonics network and currently offer two international Erasmus Mundus Joint Master’s Degree programmes in co-operation with the Department of Physics and Mathematics.

Bachelor's/Master's degrees awarded per year

Doctoral degrees awarded per year, international peer-reviewed scientific publications, news and events, university of eastern finland applies for the right to offer education in data engineering, doctoral defence of mzomwe yahya mazana, msc, 27.3.2024: mathematics teaching and learning activity model for blended instruction in tanzanian higher education, doctoral defence of annastasia shipepe, msc, 20.12.2023: a design study on educational robotics in namibia, drug-induced transcriptomics for drug discovery using both ai and classical approaches, finnish learning analytics and artificial intelligence in education conference - flaiec 2024, 4th international conference for sustainable resource society - ics24.

Events archive

Computing has a wide range of applications. As a student, you can select the most interesting courses from among those offered at the School to build your own specialisation studies. During advanced studies, you will participate in courses related to the research carried out at the School on Computing.

Computing professionals are in high demand in working life. Our graduates find employment in, for example, the software industry, public administration, retail, teaching, and research. Teacher training in computing, on the other hand, provides qualifications to work as a primary, secondary or upper secondary school teacher, as well as the abilities to work as a trainer in working life. Experts in computing are also needed in high-tech research and development.

In addition to the international programmes taught in English, the school also offers degree programmes taught in Finnish.

New student orientation

Exchange studies

Learning community of students of the School of Computing

Student organisation Serveri ry (Kuopio)

Student organisation Skripti ry (Joensuu)

Alumni network

Continuous learning

Sustainable Technologies, Bachelor's and Master's Degree (in Finnish)

Computer Science, Bachelor's and Master's Degree (in Finnish)

Business Economics and Data Science, Bachelor's and Master's Degree (in Finnish)

International Master's degree programmes

Master's Programme in Information Technology (IMPIT)

Imaging and Light in Extended Reality (IMLEX) Erasmus Mundus Japan

Computational Colour and Spectral Imaging (COSI) Erasmus Mundus

Phd studies in the school of computing.

Doctoral-level education in the School of Computing offers doctoral students high level systematic research training and give a deep insight in to the specific research area chosen by the student. The research focus areas of the School are

Artificial Intelligence

Data science.

• Future technologies

We belong to Doctoral Programme in Science, Forestry and Technology.

PhD Studies in Computer Science

Doctoral Programme in Science, Forestry and Technology (LUMETO)

The three main areas of research at the School of Computing are: artificial intelligence, data science, and future technologies. We engage in close co-operation with our partners, and the research we do is respected internationally.

The focus areas of our AI research are computational intelligence and speech as well as computational spectral imaging and machine learning. Data science is one of the modern society’s key drivers of change in both research and business. Our research is aimed at developing new methods for data analysis. Statistical methods, on the other hand, are the key tools used by researchers in all fields of science that are based on empirical data.

Research and development in future technologies focus on new solutions for learning and development. Our society is digitalising rapidly, and new skills are constantly required to operate in it. Research in learning analytics is particularly working on pushing the boundaries of methodological innovations in learning analytics. Our research in interactive technologies focuses on eye-tracking, digital accessibility and human-computer interaction (HCI). 

Dissertations

Research communities

Search research group, project or expert in UEF Connect

Open science

Our research groups

Computational Intelligence

Computational Speech Group

Machine Learning

Computational Spectral Imaging

Algorithmic Data Analysis

Nature-Inspired Machine Learning

Statistics Education and Research

Future Technologies

Interactive Technologies

Learning Analytics

Technologies for Learning and Development

Contact information

Head and deputy heads.

Markku Tukiainen

Head of Department

School of Computing, Faculty of Science, Forestry and Technology

markku.​tukiainen​@uef.fi

+358504411508

Erkki Pesonen

University lecturer.

Deputy Head of Department

erkki.​t.​pesonen​@uef.fi

+358403552562

Simo Juvaste

simo.​juvaste​@uef.fi

+358503803416

Professors and associate professors

Mohamed Abdelgalil

Associate professor.

mohammed.​saqr​@uef.fi

+358503089369

Roman Bednarik

roman.​bednarik​@uef.fi

+358414306116

Pasi Fränti

pasi.​franti​@uef.fi

+358504422265

Xiaozhi Gao

xiao-​zhi.​gao​@uef.fi

+358505147512

Markku Hauta-Kasari

1st Vice-Dean

markku.​hauta-​kasari​@uef.fi

+358504056231

Ville Hautamäki

ville.​hautamaki​@uef.fi

+358504422350

Tomi Kinnunen

tomi.​kinnunen​@uef.fi

+358504422647

Pauli Miettinen

pauli.​miettinen​@uef.fi

+358504753210

Matti Tedre

matti.​tedre​@uef.fi

+358504340376

Pekka Toivanen

pekka.​toivanen​@uef.fi

+358405439021

Senior teaching and research personnel

Nestoras antoniou, postdoctoral researcher.

nestoras.​antoniou​@uef.fi

Mikko-Ville Apiola

mikko.​apiola​@uef.fi

+358504322670

Ivan Franco Gonzalez

ivan.​franco​@uef.fi

+358504710284

Senior Researcher

pauli.​falt​@uef.fi

+358505018677

Esther Galbrun

esther.​galbrun​@uef.fi

+358504729297

Keijo Haataja

Project manager.

keijo.​haataja​@uef.fi

+358505272359

Marko Hassinen

marko.​hassinen​@uef.fi

+358504778152

Virpi Hotti

Senior university lecturer.

virpi.​hotti​@uef.fi

+358405629192

mika.​hujo​@uef.fi

+358403553601

Joni Hyttinen

joni.​hyttinen​@uef.fi

+358505054232

Matti Itkonen

matti.​itkonen​@uef.fi

+358504797075

Ilkka Jormanainen

ilkka.​jormanainen​@uef.fi

+358503757289

Marko Jäntti

marko.​jantti​@uef.fi

+358504616738

Harri Karhu

University teacher.

harri.​karhu​@uef.fi

+358505251695

juho.​kopra​@uef.fi

+358505689101

Gaetano La Russa

gaetano.​la.​russa​@uef.fi

+358504731154

Niko Lappalainen

Project researcher.

niko.​lappalainen​@uef.fi

+358504797118

Sonsoles López Pernas

sonsoles.​lopez​@uef.fi

+358504126453

Matti Nykänen

matti.​nykanen​@uef.fi

+358403553521

Nicolas Pope

nicolas.​pope​@uef.fi

+358505288449

Ismaila Sanusi

ismaila.​sanusi​@uef.fi

+358504724677

Dmitri Semenov

Research manager.

dmitry.​semenov​@uef.fi

+358503043941

Jarkko Suhonen

Staff scientist.

jarkko.​suhonen​@uef.fi

+358504358927

Hana Vrzáková

hana.​vrzakova​@uef.fi

+358503267676

Samuel Yigzaw

samuel.​yigzaw​@uef.fi

PhD students

Sami Andberg

Doctoral researcher.

sami.​andberg​@uef.fi

Muhammad Afzal

Department of Chemistry, Faculty of Science, Forestry and Technology

muhammad.​afzal​@uef.fi

Ramy Elmoazen

ramy.​elmoazen​@uef.fi

+358504767176

Maiju Karjalainen

maiju.​karjalainen​@uef.fi

+358469203579

Salman Khalil

salman.​khalil​@uef.fi

+358503208916

Federico Malato

federico.​malato​@uef.fi

Andrius Penkauskas

andrius.​penkauskas​@uef.fi

+358504391219

Mohammadhossein Salari

mohammadhossein.​salari​@uef.fi

+358505626207

Hasnain Shah

ali.​shah​@uef.fi

+358504081364

Vishwanath Singh

vishwanath.​singh​@uef.fi

Project Coordinator

md.​sultan​@uef.fi

Amos Sunday

amos.​sunday​@uef.fi

Jimi Tuononen

jimi.​tuononen​@uef.fi

+358503228402

janne.​tyni​@uef.fi

+358505717136

Administrative personnel

Marjut anttilainen, project controller.

Financial Services, University Services

marjut.​anttilainen​@uef.fi

+358504425631

Maunu Aunesluoma

maunu.​aunesluoma​@uef.fi

Aaya Bougrine

aaya.​bougrine​@uef.fi

Manasi Chhibber

Research assistant.

manasi.​chhibber​@uef.fi

Juha Hakkarainen

It specialist.

Digital Services, University Services

juha.​hakkarainen​@uef.fi

+358503726261

Laura Hurmalainen

Coordinator.

laura.​hurmalainen​@uef.fi

Jesper Kauppinen

Teaching assistant.

jesper.​kauppinen​@uef.fi

Oili Kohonen

Academic affairs specialist.

Student and Learning Services, University Services

oili.​kohonen​@uef.fi

+358503789363

Maria Kuismin

maria.​kuismin​@uef.fi

Veera Kukkola

Study secretary.

veera.​kukkola​@uef.fi

+358503434139

Jussi Kukkonen

jussi.​kukkonen1​@uef.fi

Janne Laakkonen

janne.​laakkonen​@uef.fi

Merja Leppänen

merja.​leppanen​@uef.fi

+358403552262

Veikko Miettinen

veikko.​miettinen​@uef.fi

+358504360130

Paavo Pakoma

paavo.​pakoma​@uef.fi

+358403552570

Jukka Pitkänen

jukka.​pitkanen​@uef.fi

+358404863424

Sofia Purontaus

sofia.​purontaus​@uef.fi

Olli Tiilikainen

Training officer.

olli.​tiilikainen​@uef.fi

+358505210138

Laura Toivanen

laura.​toivanen​@uef.fi

+358503135381

Miika Turja

miika.​turja​@uef.fi

Student and Learning Services

University of Eastern Finland’s Student and Learning Services is responsible for providing general study-related administrative services for students and staff, as well as offer support for applicants.

Read more:  Student and Learning Services

Joensuu Campus

Postal address

University of Eastern Finland

P.O. Box 111

FI-80101 Joensuu

Street address

Länsikatu 15

Joensuu Science Park, 3rd floor

Kuopio Campus

P.O. Box 1627

FI-70211 Kuopio

Microkatu 1

KPY Novapolis building, F and G wings, 2nd floor  

• ACRIS instructions

Department of Computer Science

• Aalto University
• School of Science
• Website https://www.aalto.fi/en/department-of-computer-science

Konemiehentie 2 , Computer Science building

Organisation profile

Profile information.

Department of Computer Science provides world-class research and education in modern computer science to foster future science, engineering and society. The work combines fundamental research with innovative applications.

The Department of Computer Science (CS) is the largest department at Aalto University and the largest CS unit in Finland with 44 professors, 20 lecturers and more than 500 employees. The department is part of the School of Science in Aalto University, located on the Otaniemi campus.

The department is known for its innovative and consistently high-quality work matching the very best teams in the world, and is a coveted partner in international collaborations. Research at the department addresses and solves challenging problems of high practical relevance with revolutionary applications.

Research.aalto.fi - our staff profiles and publications

• Professors of Practice (PoP)
• Academic staff
• Publications

Computer Science Research areas

Research in the Department of Computer Science is divided into 14 research areas.

Algorithms and Theoretical Computer Science ( Web-page ) - Fundamental methods and mathematics of computation. 

Artificial Intelligence and Machine Learning ( Web-page ) - Fundamentals and practical impact of AI on businesses and societies.

Complex Systems ( Web-page ) - Network science, stochastic processes and nonlinear dynamics.

Computational Life Sciences ( Web-page ) - Research on Computational modelling, data analysis and design of biological systems.

Computing education research and educational technology ( Web-page ) - Computer science, psychology, and education.

Computing Systems ( Web-page ) - The study, design and development of modern computing systems.

Digital Ethics, Society and Policy ( Web-page ) - Engaging the societal impact of technologies through transdisciplinary inquiry and ethical practices.

• Engineering Psychology

Human-Computer Interaction and Design ( Web-page ) - Intersection of computing, interaction technologies and human-centred design research.

Large-scale Computing and Data Analysis ( Web-page ) - Large-scale distributed/parallel systems and big data analysis and management.

• Quantum Software and Algorithms

Security and Privacy ( Web-page ) - Secure and private computing and communication systems.

Software and Service Engineering ( Web-page ) - Designing, developing, and operating software systems and digital services.

Visual Computing ( Web-page ) - Creation and understanding of images, shapes, and 3D spaces.

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Our work contributes towards the following SDG(s):

Fingerprint

• Models Computer Science 100%
• User Computer Science 57%
• Networks Computer Science 49%
• Algorithms Computer Science 45%
• Application Computer Science 40%
• Design Computer Science 35%
• Service Computer Science 23%
• Contexts Computer Science 22%

Collaborations and top research areas from the last five years

Dive into details.

Select a country/territory to view shared publications and projects

Shamsiiat Abdurakhmanova

• Department of Computer Science - Postdoctoral Researcher
• Professorship Jung Alexander - Postdoctoral Researcher

Person: Postdoctoral researchers

• Professorship Guckelsberger Christian - Postdoctoral Researcher

• Department of Computer Science - Visitor (Faculty)
• Professorship Aura Tuomas - Visitor (Faculty)

Person: Visiting Scholar

• 1 Not started
• 362 Finished

Projects per year

MATINE 3D-mallinnus/Kannala: Sisätilojen ja avoimen ympäristön 3D-mallinnus maanpuolustuksessa

Kannala, J.

01/04/2024 → 31/12/2025

Project : Other external funding: Other government funding

Mobility COSMAGG/Korpi-Lagg: Investigating COSmic MAGnetism with GPU-accelerated codes

Korpi-Lagg, M.

01/04/2024 → 31/03/2026

Project : Academy of Finland: Other research funding

• Star Formation 100%
• Calculation 100%
• Parameter 100%
• Interstellar Matter 100%
• Magnetohydrodynamic Generator 100%

-: ELASTIC WP 1/Gunn

01/03/2024 → 28/02/2027

Project : EU other competitive funding (ERA-NET, EURAMET, EUREKA, EU JU)

Research output

• 6674 Conference article in proceedings
• 4230 Article
• 844 Working paper
• 639 Chapter
• 366 Doctoral Thesis
• 162 Anthology
• 138 Software
• 105 Abstract
• 88 Review Article
• 64 Conference article
• 55 Editorial
• 38 Commissioned report
• 34 Licenciate's thesis
• 30 Other contribution
• 28 Meeting Abstract
• 28 Special issue
• 18 Master's thesis
• 15 Comment/debate
• 12 Foreword / postscript
• 7 Digital or Visual Products
• 6 Exhibition
• 4 Literature review
• 3 Data Article
• 3 Blog article
• 2 Entry for encyclopedia / dictionary
• 2 Other chapter contribution
• 1 Book/Film/Article review
• 1 Performance

Research output per year

A comparative study of clinical trial and real-world data in patients with diabetic kidney disease

Research output : Contribution to journal › Article › Scientific › peer-review

• Real-World Data 100%
• Randomized Controlled Trial 100%
• Data Set 33%
• Cluster Analysis 22%
• Control 11%

A modified version of the MDRANGE software for calculations in nuclear material physics

Research output : Contribution to conference › Abstract › Scientific

• Deposited Energy 100%
• Physics 100%
• Materials Science 100%

Applicability and Robustness of Deep Learning in Healthcare

Research output : Thesis › Doctoral Thesis › Collection of Articles

• Healthcare 100%
• Deep Learning 100%
• Devices 100%
• Neural Network 60%
• Performance Art 20%

References to Qing China Technology and Industry in Finnish Technical Journals 1880-1912

La Mela, M. (Creator) & Fridlund, M. (Creator), Zenodo, 2019

DOI : 10.5281/zenodo.2607892

This is accompanying conference participation datasets for the PLoS ONE 2016/11(2) publication "A Theoretical Model for the Associative Nature of Conference Participation"

Smiljanić, J. (Creator), Chatterjee, A. (Creator), Kauppinen, T. (Creator) & Mitrović-Dankulov, M. (Creator), figshare, 2016

DOI : 10.6084/m9.figshare.2066907.v2 , https://figshare.com/articles/Conference_Datasets/2066907

IoT devices captures

Marchal, S. (Creator), Aalto University, 3 Apr 2017

DOI : 10.24342/285a9b06-de31-4d8b-88e9-5bdba46cc161

2016 CPHC/BCS Academy of Computing Distinguished Dissertation runner-up

Micallef, Luana (Recipient), 2016

Prize : Award or honor granted for a specific work

• Computing 100%

2016 De Groot Prize

Vehtari, Aki (Recipient), 16 Jun 2016

2016 Learning Contribution Achievement Award 2016

Kauppinen, Tomi (Recipient) & Malmi, Lauri (Recipient), 20 May 2017

• Teaching 100%
• Contribution 100%
• Achievement 100%
• Learning 100%
• Awards 100%
• 1063 Visiting an external academic institution
• 1061 Hosting an academic visitor
• 604 Membership of a scientific or program committee of a conference or seminar
• 420 Invited academic talk
• 393 Membership of an editorial board
• 373 Position of trust in a society or network
• 364 Conference presentation
• 257 Reviewer of scientific journal
• 188 Pre-examination of doctoral thesis or acting as opponent to doctoral students or membership of doctoral thesis committee or board
• 129 Participant of a conference, workshop, session, tutorial or event
• 122 Organization of a workshop, panel, session, tutorial or event
• 111 Membership of an organizing committee or other positions of trust of a conference or a seminar
• 104 Position of trust in an academic community
• 102 Chair or participant of a conference, workshop, panel, session, tutorial or event
• 99 Reviewer of conference article
• 83 Academic keynote or plenary lecture
• 56 Evaluation of funding applications
• 53 Statement of assessment of professorship
• 34 Non-academic public talk (public outreach)
• 31 Regular membership of a society or network
• 23 Position of trust in a funding organization
• 16 Evaluation of significant academic activities
• 14 Statement of assessment of docentship
• 14 Membership of a competition jury
• 12 Work for advisory/policy/evaluation group or panel (public/government/UN/EU)
• 7 Organization of an event for a non-academic audience (public outreach)
• 7 Other expert tasks or merit
• 6 Consultancy and expert advice (including statements)
• 6 Evaluation of other academic activities
• 6 Studying in an external academic institution
• 5 Appointed supervisor or advisor of postgraduates at Higher Education Institutions.
• 4 Statement of assessment of other positions
• 4 Academic qualification (e.g. title of docent)
• 4 Teaching merits
• 3 Work for advisory/policy/evaluation group or panel (non-public/non-government/industry)
• 2 School engagement
• 2 Reviewer of other manuscript
• 2 Appointed supervisor or advisor of undergraduates at Higher Education Institutions.

Activities per year

Data Science for Transportation (Journal)

Zhiren Huang (Reviewer)

Activity : Publication peer-review and editorial work types › Reviewer of scientific journal

External examiner of PhD thesis

Samuel Kaski (Examiner)

Activity : Academic assessments › Pre-examination of doctoral thesis or acting as opponent to doctoral students or membership of doctoral thesis committee or board

Elämisen suuret kysymykset videopeleissä

Heidi Rautalahti (Speaker)

Activity : Talk or presentation types › Public talk to non-academic audience (public outreach)

Press/Media

Älypuhelimesta luopuminen hankaloittaa elämää, mutta osa on valmis tilaamaan yksinkertaisen puhelimen afrikasta asti – suomalaistutkimus selvitti, miksi.

Janne Lindqvist

1 item of Media coverage

Press/Media : Media appearance

Tekoäly pitää saada ymmärtämään ihmistä paremmin

Antti Oulasvirta

Tutkimuksen mukaan tekoäly ei ymmärrä ihmistä riittävästi

Department of Computer Science

Department information, graduate schools.

A great part of the PhD students belong to some graduate school. The graduate schools organise PhD courses, and offer some positions for full-time PhD researchers. The department participates in four PhD schools and a large network of research schools. The graduate schools have their own admission processes so please see the web page of the schools on how to apply.

Helsinki Graduate School in Computer Science and Engineering: Hecse

The Helsinki Graduate School in Computer Science and Engineering (Hecse) is a PhD programme in computer science and engineering jointly offered by the Aalto University and the University of Helsinki.

Research fields: Algorithms, Structures and Complexity; Pattern Analysis and Intelligent Systems; System Software

Director: Professor Hannu Toivonen Coordinator: Research Coordinator Greger Lindén Web page: http://www.cs.helsinki.fi/hecse/ Email: [email protected]

Finnish Graduate School in Computational Sciences: FICS

The Finnish Graduate School in Computational Sciences, FICS, is jointly offered by 11 universities in Finland, including the University of Helsinki.

Research fields: Computational Statistics and Information Technology, Numerical and Applied Mathematics, Computational Physics, Computational Biology, Future Computational Sciences (economics, medicine, agriculture, humanities, ecology, neuroscience etc.) .

Director: Professor Samuel Kaski Coordinator: Ella Bingham Web page: http://fics.hiit.fi Email: [email protected]

Future Internet Graduate School: FIGS

The Future Internet Graduate School, FIGS, is jointly offered by the Aalto University, University of Helsinki and Tampere University of Technology.

Research fields: Future Internet research including economical, social, legal, and behavioural issues.

Director: Professor Martti Mäntylä Web page: http://figs.hiit.fi Email: [email protected]

The Graduate School on Software Systems and Engineering (SoSE)

The Graduate School on Software Systems and Engineering (SoSE) is a national PhD programme jointly offered by the University of Helsinki, Aalto University, Tampere University of Technology, the University of Oulu, Åbo Akademi University and the University of Tampere

Research field: software engineering, software processes, software design and architecting, software implementation, software testing, software maintenance, software project management, software quality assurance; information systems, benefits of information technologies, quality of information systems development processes

Director: Professor Kai Koskimies , Tampere University of Technology Coordinator: Maarit Harsu Web page: http://www.cs.tut.fi/~sose/ Email: [email protected]

Network of Finnish Graduate Schools in Information Technology (Figsit)

The Network of Finnish Graduate Schools in Information Technology (Figsit) is an informal collaboration forum for PhD schools in computer science and information technology around the country. In addition to Hecse, other members are Comas (Jyväskylä), ECSE (Eastern Finland), Infotech (Oulu), TISE (Tampere) and TuCS (Turku).

All the PhD courses are open to network members, and they are announced on a centralized web page. The network arranges an annual international summer school as well as other events for peer support for students and teachers, as well as networking support

Other related graduate schools

Doctoral admissions

You can search for doctoral programmes on the application portal Studyinfo.fi , or you can contact the universities directly about the doctoral study and research options they offer. Check the application times and procedures, eligibility requirements and other details with the university you are interested in. The following links will take you to each university's Doctoral studies and research info pages.

• Aalto University
• University of Helsinki
• University of Eastern Finland
• University of Jyväskylä
• University of Lapland
• LUT University
• University of Oulu
• Hanken School of Economics
• University of the Arts Helsinki
• Tampere University
• University of Turku
• University of Vaasa
• Åbo Akademi University  

Doctoral / PhD funding opportunities

See the advice on doctoral level scholarships  to learn where you can search for doctoral level research funding. The universities may also offer paid doctoral and post-doc positions, see below.

Academic research positions and jobs

Announcements for doctoral and post-doc researcher positions at Finnish universities can also be found on academic recruitment sites like:

• Jobs in Finland / Academic
• Academicpositions.fi

Early career researcher info & advice

Information, advice and guidelines for early career researchers - compiled by the Finnish Union of University Researchers and Teachers (FUURT)

Scientific research in Finland

Research.fi is a service offered by the Ministry of Education and Culture where you can learn more about the Finnish science and innovation system and policy, and research conducted in Finland.

• Research.fi

Doctoral Programme of Computing and Electrical Engineering

Research-oriented professionals address global challenges, degree earned, planned duration.

Information processing, digitalisation and the electrification of energy systems have emerged as major drivers of competitiveness in a number of sectors. Data and energy are used everywhere from farms to hospitals and the commercial sector. Research that is carried out in the fields of the doctoral programme paves the way for the increasingly effective and automated production, storage and utilisation of data and energy. A number of scientific disciplines underpin the development of data analysis, digitalisation and electrification: mathematics, informatics and statistics from a methodological perspective, and electronics, signal processing, electrical engineering, communications and positioning technology, and information technology from a technological perspective. Data networks, information security, machine learning, software as well as information and automation systems are essential for the functioning of modern societies. The need to make smarter use of limited resources is continuously creating new and exciting challenges in our fields of research.

The Doctoral Programme of Computing and Electrical Engineering will provide you with excellent theoretical knowledge and technological skills and empower you to pursue a career in research or technology development or take on specialist roles in a variety of organisations. With data, computing, data communications, electronics and automation systems that rely on electricity woven into the fabric of everyday life, there is considerable demand worldwide for professionals with broad knowledge of these technologies. Researchers at Tampere University carry out world-class research in the fields covered by the doctoral programme and develop ground-breaking, cross-disciplinary solutions for building the digital society of tomorrow. An in-depth understanding of not only the digital operating environment but also energy and ecological efficiency are the keys to addressing some of the greatest challenges of our time, such as globalisation, climate change and urbanisation. 

Detailed information on the content and structure of the studies is included in the curriculum.

Become a student

Learn more about the studies, admissions, and eligibility criteria on Studyinfo. In addition, applications are submitted via the Studyinfo.fi service.

Carefully read through the admissions requirements in Studyinfo.fi before applying. For additional questions on applying, application documents and application process, please contact our Admissions office . For questions regarding the content of the programme, please contact itc.doc.tau(a)tuni.fi.

To find a responsible supervisor, start checking the  available doctoral research areas and contact professors (pdf) . Once you've found a responsible supervisor, consider together other possible supervisors or members of a follow-up group.

For more information on applying

Give us feedback.

We continue to build our site and welcome all feedback. Please also let us know if you spot any mistakes on our site. If you wish to get a reply, remember to include your email address in your feedback message.

If you have questions about studying with us, please contact [email protected] (Tampere University) or [email protected] (Tampere University of Applied Sciences). If you have problems with your user account or other IT-related issues, get in touch with our IT Helpdesk

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Your last viewed study options

Degree programme   Postgraduate degree (University)

Degree earned: Doctor of Philosophy , Doctor of Science (Technology)

Planned duration: 4 years

City: Tampere

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31 funded phd programs in computer science at helsinki ict, finland.

Helsinki ICT, Finland invites online application for multiple fully funded PhD Programs in Computer Science. Candidates interested in fully funded PhD positions can check the details and may apply as soon as possible. The online application form opens on December 12, 2022 and closes on January 29, 2023 11:59pm Finnish time.

The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) is a joint initiative by Aalto University and the University of Helsinki, the two leading universities within this area in Finland. The network involves at present over 80 professors and over 200 doctoral students, and the participating units graduate altogether more than 40 new doctors each year.

The participating units of HICT have currently funding available for qualified doctoral students. We offer an exciting opportunity to join world-class research groups, with 30 research projects to choose from. The activities of HICT and the themes of open positions are structured along five research area specific tracks:

• Algorithms and machine learning
• Life science informatics
• Networks, networked systems and services
• Software and service engineering and systems
• User centered and creative technologies

If you wish to be considered as a new doctoral student in HICT you can apply to one or a number of doctoral student positions. We actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

1. PhD Programs in Explainable and robust AI for scientists 

Summary of phd positions:.

Machine learning and AI are extensively used in the sciences. When modelling physical systems, the understandability and statistical robustness of the models is often more important than predictive accuracy. We are looking for talented postdoctoral researchers and doctoral students to study explainable and understandable AI and the uncertainty quantification of AI models. While the AI methods we develop are generic and not tied to any specific application domain, we work closely with scientists to build Virtual Laboratory for Molecular Level Atmospheric Transformations.

2. PhD Programs in Green NLP – controlling the carbon footprint in sustainable language technology

GreenNLP addresses the problem of increasing energy consumption caused by modern solutions in natural language processing (NLP). Neural language models and machine translation require heavy computations to train and their size is constantly growing, which makes them expensive to deploy and run. In our project we will reduce the training costs and model sizes by clever optimizations of the underlying machine learning algorithms with techniques that make use of knowledge transfer and compression.

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3. PhD Programs in Bayesian Workflows for Iterative Model Building and Networks of Models

Statistical analysis is critical when it comes to obtaining insights from data. Despite the practical success of iterative Bayesian statistical model building, it has been criticized to violate pure Bayesian theory and that we may end up with a different model had the data come out differently. In this project, we formalize and develop theory and diagnostics for iterative Bayesian model building. The practical workflow recommendations and diagnostics guide the modeller through the appropriate steps to ensure safe iterative model building, or indicate when the modeler is likely to be in the danger zone.

4. PhD Programs in Trust-M: Designing Inclusive & Trustworthy Digital Public Services for Migrants in Finland

10 best platforms to create and sell online courses in 2022, 5. phd programs in neuro-symbolic multiagent reinforcement learning.

Researchers interested in postdoctoral/doctoral positions are invited to apply for this project. Here, we shall study a new approach to synthesis of efficient communication schemes – including learning of novel concepts – in cooperative multi-agent systems, trained via reinforcement learning. We combine symbolic methods with machine learning, in what is referred to as a neuro-symbolic system, where a neural network learns to produce programs in a symbolic language to solve a task at hand.

6. PhD Programs in Machine learning for wireless and mobile systems

Machine learning (ML) is the foundation of artificial intelligence in today’s applications. The scope of ML is wide, including (but not limited to) speech synthesis and recognition, machine translation, and computer vision. This project involves designing and (or) applying ML techniques to the scenarios represented by wireless and mobile systems.

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7. phd programs in cloud-native systems: software systems and security.

Cloud computing has introduced a new model for provisioning resources and services over the Internet on top of a virtualized, elastic infrastructure. Additional paradigms have also recently emerged: edge, fog, and serverless. Moreover, modern (namely, cloud-native) applications are generally composed of multiple microservices, realized as software containers and managed through an orchestrator such as Kubernetes. This project involves addressing different aspects of cloud-native systems, with focus on software systems and security.

8. PhD Programs in Transformer methods for hyperspectral image fusion

The project will study and develop new Transformer-based models for modeling of multispectral, hyperspectral and lidar based remote sensing data in varying resolutions. We will analyze the information content of modern and future hyperspectral earth observation and lidar data obtained over the boreal forest zone. The results will be used for studying the biodiversity of forests based on the distrubution of tree species and the spatial arrangements of individual trees. In addition, the models will be included in a digital twin of the Finnish forests. The project aims at novel methodological and algorithmic improvements by using state-of-the-art Transformer models that have already been studied and developed in the research group.

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9. phd programs in transformative ai collaborations.

Aalto University has launched a new collaborative initiative in transformative research to support cooperation between AI and Data Science researchers with researchers in other fields. The aim is to create new knowledge by developing AI methods to advance research in other domains. We are especially seeking candidates interested in collaborations between Artificial Intelligence and/or Data Science for Transformative AI collaborations.

10. PhD Programs in Machine learning for drug design

Recent progress in machine learning for generative and predictive models of molecules brings us towards computational, automatised drug design. We develop statistical methods and models for molecular structures, energies and interactions with the help of deep learning. A number of open problems reside in developing neural network models with physics-based inductive biases, in generative models in 3D spaces, in modelling the property landscapes of molecules, and in generalizing outside the training distribution in molecular design.

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11. phd programs in probabilistic modelling and bayesian inference for machine learning.

I am looking for a new doctoral student in my team which develops probabilistic modelling and Bayesian inference methods. The team has several exciting new machine learning formulations we work on, and opportunities for applying the methods with top-notch collaborators. But the core is always development of new methods, and with this call I am looking for talented researchers with background in machine learning, stats or CS (or other directly relevant topics) who are keen on developing the new methods. In the cover letter, let me know what you are interested in – if we are already working on it, all the better, but I am willing to listen to new ideas too.

12. PhD Programs in Multi-agent modeling for human-AI interaction

We develop AI assistants which help people make better decisions, with ongoing applications in science and engineering. We use multi-agent formalisms to define the assistance problems these assistants solve, including the human being assisted, and employ models of human behavior to (pre-)train them in silico. We build models of human behavior using (multi-agent) reinforcement learning, based on theories of human behavior from cognitive science. In this project you will develop novel multi-agent formalizations of assistance and create new models of human behavior for these formalizations. The focus will be on maximally autonomous assistants — assistants that automate a person’s task as much as possible, while using a minimal interaction to learn to solve said person’s task well.

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13. phd programs in maximally autonomous ai.

In this project, you will develop principles and methods for AI assistants to be maximally autonomous. The goal is to automate to the greatest extent possible, while using a minimal interaction with the person being assisted. Interaction is important to learn the person’s goal, but should be done sparingly. It should only happen when necessary, i.e. when it can help reduce uncertainty about the goal and improve the assistant’s long-term decision making.

14. PhD Programs in Deep learning with differential privacy

Differential privacy allows developing machine learning algorithms with strong privacy guarantees. Recent work shows it is possible to combine strong privacy and high accuracy by pre-training models on public data and only fine-tuning the model with the sensitive data. However, high accuracy still requires care for example in hyperparameter tuning. The aim of this project is to develop methods that make it easier to train high accuracy private models. The project will benefit from a very large grant of compute time on LUMI, 3rd fastest supercomputer in the world. The project requires a background in deep learning.

15. PhD Programs in Learning-based control for neural speech synthesis

This project aims to develop an interactive and intuitive neural speech synthesizer that encompasses both explicit control using signal processing and latent control using learning-based methods. Easy-to-use control beyond text will enable users, such as voice artists, creators, and linguists to interactively modify and adjust their voice synthesizer output. The ideal candidate for this doctoral research project has a strong background in machine learning and a working knowledge of deep learning Python libraries (such as PyTorch). Further background in speech and/or audio signal processing, C++ DSP programming, and a general interest in sound synthesis is beneficial.

16. PhD Programs in Machine Learning for Health (ML4H)

Accumulation of massive amounts of health data has enabled researchers to address questions such as: how to accurately predict the risk of disease, how to personalize treatments based on real-time data from wearable devices, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include noisy data, multiple heterogeneous data sources including images and text, learning about causality, interpreting the models, and quantifying the uncertainty, to name a few. We tackle these by developing models and algorithms which leverage modern machine learning principles.

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17. phd programs in amortized inference for experimental design and decision making.

We develop amortized experimental design and inference techniques that take into account the down-the-line decision making task. For example, this may include delayed-reward decision making where data has to be measured, at a cost, before making the decision. This problem occurs in the design-build-test-learn cycles which are ubiquitous in engineering system design, and experimental design in sciences and medicine. The solutions need Bayesian experimental design techniques able to work well with simulators, measurement data and humans in the loop, who are both information sources and the final decision makers.

18. PhD Programs in Amortized surrogates for simulation and inference

Recent advances in machine learning have shown how powerful emulators and surrogate models can be trained to drastically reduce the costs of simulation, optimization and Bayesian inference, with many trailblazing applications in the sciences. In this project, the candidate will join an active area of research within several FCAI groups to develop new methods for simulation, optimization and inference that combine state-of-the-art deep learning and surrogate-based kernel approaches – including for example deep sets and transformers; normalizing flows; Gaussian and neural processes – with the goal of achieving maximal sample-efficiency (in terms of number of required model evaluations or simulations) and wall-clock speed at runtime (via amortization).

19. PhD Programs in Deep learning for material science

The goal of the project is to develop novel deep learning algorithms to answer open questions in nanoscale physics such as predicting molecular structure in liquids or developing accurate, but very fast models of water. In particular we want to take advantage of the capabilities of Graph Neural Networks to provide robust and highly efficient simulators for molecular systems. These models will be coupled to state-of-the-art experimental characterisation, ultimately including a dynamic interaction where simulations are actively used to focus on information rich regions during experiments. We are looking for applicants with a strong background in deep learning and/or physical simulations.

20. PhD Programs in Deployment as a fundamental ML challenge

Machine learning (ML) is now used widely in sciences and engineering, in prediction, emulation, and optimization tasks. Contrary to what we would like to think, it does not work well in practice. Why? Because the conditions during deployment may radically differ from training conditions. This has been conceptualized as distribution shift or sim-to-real gap, and a particularly interesting challenge which we will be tackling is changes due to unobserved confounders. Solving this challenge is imperative for widespread deployment of ML. In this project, we will consider deployment as a fundamental machine learning challenge, developing new principles and methods for tackling this problem which can be argued to be the main show-stopper in making machine learning seriously useful in solving the real problems we are facing, in sciences, companies and society.

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21. phd programs in efficient probabilistic modeling of speech.

Probabilistic models are essential for capturing the stochastic properties in speech and audio signals. Speech synthesis in particular has recently emerged as a proving ground application for deep generative models. Autoregressive (AR) density models, such as WaveNet, achieve high quality, can be trained effectively using maximum likelihood, and have low algorithmic latency suitable for real-time applications. However, available implementations of AR inference using GPUs and popular Python libraries are slow, which has shifted research to feedforward models for parallel inference.

22. PhD Programs in Evaluating and improving posterior inference for difficult posteriors

Both MCMC and distributional approximation algorithms (variational and Laplace approximations) often struggle to handle complex posteriors, but we lack good tools for understanding how and why. We study diagnostics for identifying the specific nature of the computational difficulty, seeking to identify e.g. whether the difficulty is caused by narrow funnels or strong curvature. We also develop improved inference algorithms that account for these challenges, e.g. via automated and semi-automated transformations for making the posterior easier or by better accounting for the underlying geometry. We are looking for applicants with a strong inference background and interest in working on improving inference for the hardest problems.

23. PhD Programs in Explainable AI for virtual laboratories

FCAI is actively developing methods and software for virtual laboratories to enable AI assistance in the research process. We are looking for a candidate to research explainable AI and uncertainty quantification. Efficient human-AI collaboration requires methods that are either inherently capable of providing explanations for the decisions or can explain the decisions of other AI models. For instance, the user needs to know why AI recommends a particular experiment or predicts a specific outcome. They should always be aware of the reliability of the AI models. You will conduct the project with a team of AI researchers with access to researchers specialised in various application areas. The applicant should be interested in incorporating the techniques as part of virtual laboratory software developed at FCAI for broad applicability.

24. PhD Programs in Foundation models for interactive computing

Foundation models such as GPT-3 and CLIP are revolutionizing how AI is developed and applied, by providing reusable and general-purpose building blocks with unprecedented capabilities. Instead of training large-scale models from scratch for thousands or millions of GPU hours, one can solve novel tasks by combining pretrained foundation models in novel ways, or finetuning them for downstream tasks. The release of OpenAI’s CLIP, for instance, soon led to the emergence of CLIP-guided image generation models such as Disco Diffusion, Stable Diffusion, Midjourney, and DALL-E 2, as well as smaller-scale experiments such as ClipDraw and StyleClipDraw. CLIP also increasingly empowers various semantic search solutions across multiple industries.

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25. phd programs in language-empowered world models for rl.

Incorporating accurate causal knowledge about an environment in terms of objects and their relationships into the world model of a reinforcement learning agent can yield significant improvements in reducing the amount of exploration required to solve new tasks and to generalize to new environments. Recently, causal representation learning has been proposed as a way of extracting and representing such causal knowledge from previous experience in the latent space. However, in practice data are extremely correlated and it is not straightforward to come up with ways to even disentangle relevant objects, not to mention the causal relationships between them.

26. PhD Programs in Long term planning with search graphs

In this project, we investigate novel extensions of MCTS for continuous domains based on search graphs. Our approach is built on the idea that sharing the same action policy between several states can yield efficient planning and thus high performance. This results in a limited number of stochastic action bandit nodes to produce a layered graph instead of an MCTS search tree allowing for long term planning. The designed algorithms can be used for robotic manipulation and navigation, for example, with a Boston Dynamics Spot robot. We are looking for applicants with a strong background in reinforcement learning (especially model-based) and tree search algorithms.

27. PhD Programs in ML4Science

Machine learning is increasingly being used as a key element in research, for instance to efficiently approximate computationally costly simulations, automate design of experiments, and for integrated analysis of experimental results and multi-fidelity simulations. Much of the practical work is done in the context of specific applications in science, but our interest lies in the more general question of how ML could be used as part of the research process, essentially to improve the results and the scientific process itself. We seek solutions that work across multiple disciplines and applications.

28. PhD Programs in Multi-agent RL for collaborative AI

We develop the new ML principles and methods needed by AI assistants to help people make better decisions, with ongoing applications in science and engineering. We use multi-agent formalisms to define the assistance problems these assistants solve, including the human agent being assisted, and develop new multi-agent RL solutions for the problem. We are particularly interested in (1) how to build and (pre-)train models of human behaviour based on cognitive science, and (2) how to solve new ad-hoc teamwork problems with multi-agent RL.

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29. phd programs in private federated learning.

Many applications of machine learning require training on distributed data while keeping the data private. Private federated learning enables this, but its communication requirements can be impractical. The aim of this project is to develop new approaches and methods for private learning on distributed data. A strong background in differential privacy and/or federated learning is an asset for this project.

30. PhD Programs in Sustainability in computing

Sustainability is important in the context of AI and in particular there exist AI/ML/DS methods to improve sustainability of a given system using say AI methods. However, the computational methods used in AI are not always sustainable as they might require a lot of data or a lot of computational power produce the results. The aim of this research project is to take a look at sustainable AI methods and in particular sustainable computational methods whose energy fingerprint is minimal. One possible approach that has recently been studied is the clever use of parallel and distributed algorithms to decrease the amount of energy per flop. We are looking for applicants with interests in energy efficient or otherwise sustainable computational methods in general.

31. PhD Programs in Workflows for better priors

Bayesian models rely on prior distributions that encode knowledge about the problem, but specifying good priors is often difficult in practice. We are working on multiple fronts on making it easier, with contributions to e.g. prior elicitation, prior diagnostics, prior checking, and specification of priors in predictive spaces. We welcome applicants looking to work on any of these aspects and contribute to both theoretical development and practical software for aiding the prior specification process.

How to Apply?

Read through the list of available positions and select the topic(s) that you are interested in. Click on the link of eRecruitment system to apply to the topic that you are interested in. You can apply directly to one or multiple research projects. All the supervisors you indicate on your application form will be informed of your interest, and others also have access to your application documents.

Eligibility Criteria

The HICT calls are targeted to prospective new doctoral students who are willing to start their doctoral studies in Aalto University or University of Helsinki under one of the HICT supervisors . Current funded doctoral students in Aalto University or University of Helsinki cannot participate in this call. While all applicants who have submitted an application by the deadline will be appropriately considered, Aalto University and the University of Helsinki reserve the right to consider also other candidates for the announced positions.

If you become selected as a new doctoral student, you need to apply for a study right for doctoral studies either at Aalto University or the University of Helsinki. Your new supervisor will assist you with the application. HICT itself does not award doctoral study rights or doctoral degrees. All doctoral students within the HICT network are doctoral students of either Aalto University or the University of Helsinki.

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Compulsory attachments.

All materials should be submitted in English in a PDF format. Note: files should be 5MB max. You can upload multiple files to the eRecruitment system, each 5MB.

1. Letter of motivation (max. tw0 pages). Please describe your background and future plans, and in particular the reasons for selecting the project(s) (you can get more information on the projects and supervisors through their web pages). Try to make your motivation letter as convincing as possible, so that the potential supervisors get interested. You do not have to write several motivation letters in case you apply for multiple projects, but if you prefer you can attach separate letters for individual projects.

2. A curriculum vitae and list of publications with complete study and employment history (please see an example CV at Europass pages)

3. A study transcript provided by the applicant’s university that lists studies completed and grades achieved.

4. A copy of the M.Sc. degree certificate . In the Finnish university system, a person must have a Master’s degree in order to enroll for doctoral studies. If the degree is still pending, then a plan for its completion must be provided. (The letter describing the completion plan can be free-format)

5. Contact details of possible referees from 2 senior academic people. We will contact your referees, if recommendation letters are required.

Application Deadlines: January 29, 2023 11:59pm Finnish time

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• 10 April 2024

Randomness in computation wins computer-science ‘Nobel’

• Davide Castelvecchi

You can also search for this author in PubMed   Google Scholar

Avi Wigderson received the Turing Award for his foundational contributions to the theory of computation. Credit: Dan Komoda

A leader in the field of computational theory is the latest winner of the A. M. Turing Award, sometimes described as the ‘Nobel Prize’ of computer science.

Avi Wigderson at the Institute for Advanced Study (IAS) in Princeton, New Jersey, is known for work straddling several disciplines, and had already won a share of the Abel Prize , a top mathematics award, three years ago.

He receives the Turing Award “for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science”, the Association for Computing Machinery (ACM) in New York City announced on 10 April.

“I was extremely happy, and I didn’t expect this at all,” Wigderson tells Nature . “I’m getting so much love and appreciation from my community that I don’t need prizes.”

‘A towering intellectual force’

Wigderson was born in Haifa, Israel, in 1956. He studied at Technion — Israel Institute of Technology in Haifa and later at Princeton University; he has been at the IAS since 1999. He is known for his work on computational complexity — which studies how certain problems are inherently slow to solve, even in principle — and on randomness in computation. Many practical algorithms make random choices to achieve their objectives more efficiently; in a series of groundbreaking studies in the 1990s, Wigderson and his collaborators showed that conventional, deterministic algorithms can, in principle, be roughly as efficient as ‘randomized’ ones 1 . The results helped to confirm that random algorithms can be as accurate as deterministic ones are.

“Wigderson is a towering intellectual force in theoretical computer science,” said ACM president Yannis Ioannidis in a statement. In addition to Wigderson’s academic achievements, the ACS cited his “friendliness, enthusiasm, and generosity”, which have led him to be a mentor to or collaborate with hundreds of researchers worldwide. Wigderson admits that he is a “big proselytizer” of the intellectual pleasures of his discipline — he wrote a popular book about it and made it freely available on his website . “I think this field is great, and I am happy to explain it to anybody.”

The Turing Award is named after the celebrated British mathematician and code-breaker Alan Turing (1912–54), who in the 1930s laid the conceptual foundations of modern computing. “I feel completely at home with mathematics,” says Wigderson, adding that as an intellectual endeavour, theoretical computer science is indistinguishable from maths. “We prove theorems, like mathematicians.”

doi: https://doi.org/10.1038/d41586-024-01055-y

Impagliazzo, R. & Wigderson, A. in Proc. 29th ACM Symposium on Theory of Computing 220–229 (ACM, 1997).

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Entry requirements

To be eligible for entry, you'll need:

• a bachelor's degree (or equivalent) in computer science or software engineering, or
• a bachelor's degree (or equivalent) which includes all of the relevant discipline content (see below), or
• to have completed post-secondary studies and 2 years full-time equivalent relevant work experience (see below). Applications based on post-secondary study and/or work experience will be individually assessed.

Relevant disciplines for previous qualifications

If your bachelor's degree was not awarded in computer science or software engineering, you must have successfully completed all of the following discipline content in your tertiary studies:

• data structures and algorithms
• at least 2 programming courses
• discrete mathematics or algebra
• at least 3 from the following:
• computer organisation or computer architecture or computer systems
• computer networks or communication networks
• operating systems
• databases or information systems
• probability and statistics

Relevant work experience

Relevant work experience includes professional experience in software development or engineering, cyber security analysis or architecture, data engineering or science, machine learning, computer networks or statistical analysis that involves programming experience and work experience in at least two of the following areas:

• probability and statistics 

This will need to be supported with evidence. 

Evidence of relevant work experience should include a letter from your employer (and/or previous employers) clearly stating the following:

• That you work (or worked) within the specified organisation
• The nature of your work, detailing any relevant duties and responsibilities to the entry criteria above
• The length of time you were in your role/s (i.e demonstrating minimum length for entry) and whether this was full-time, part-time, or casual
• Any further bespoke conditions listed by the entry criteria

Letters will typically be expected to be presented on company letterhead and signed by a manager or HR representative. A CV or resume is not a sufficient document on its own, and must be accompanied by a supporting letter as described above.

All applications based on work experience are subject to an individual assessment.

Entry into a program through work experience does not necessarily provide a pathway into further study in a Masters.

GPA equivalent

Select where you studied and your qualification to see the GPA equivalent you need to be considered for this program.

Use the GPA equivalent as a guide. When you apply, we’ll calculate your GPA using the UQ grading scale. Any failing grades will be included. Entry requirements are subject to change.

Equivalent subjects

Related programs.

Depending on your previous qualifications and current goals, you might want to consider one of these related programs:

• Master of Computer Science
• Master of Computer Science (Management)
• Graduate Certificate in Computer Science

English language requirements

IELTS overall 6.5; reading 6; writing 6; speaking 6; listening 6. For other English Language Proficiency Tests and Scores approved for UQ

TOEFL iBT (including Paper Edition) - Overall 87, listening 19, reading 19, writing 21 and speaking 19.

PTE Academic - Overall Score of 64 and 60 in all sub bands.

BE - A minimum overall grade of 4 plus a minimum grade of C in all macro skills.

CES - Overall 176 and 169 in all sub bands.

OET is not accepted.

There are other ways to meet the English language requirements. For some programs, additional conditions apply.

Learn how to meet the English language requirements

Student visas

International students who are accepted into full-time study in the Graduate Diploma in Computer Science are eligible to apply for an Australian student visa (subclass 500).

There are a number of requirements you must satisfy before a visa is granted, including the Genuine Student (GS) requirement.

Learn more about student visas

Fees and Scholarships

Indicative annual fee.

Approximate yearly cost of tuition (16 units). Your fees will vary according to your selected courses and study load. Fees are reviewed each year and may increase.

Fee information for 2025 is not yet available. Fee information displayed is for 2024.

Learn more about postgraduate fees

Approximate yearly cost of full-time tuition (16 units). Your fees will vary according to your study load. Fees are reviewed each year and may increase.

AUD $53,760

Government assistance, financial aid.

As an international student, you might be eligible for financial aid – either from your home country, or from the Australian Government.

Learn more about financial aid

Domestic places in the Graduate Diploma in Computer Science are Commonwealth Supported. This means the cost of your education is shared between you and the Australian Government.

Instead of tuition fees, Commonwealth Supported students pay what are called student contribution amounts.

HECS-HELP is an Australian Government loan scheme to assist eligible students with the cost of their student contribution amounts.

Learn more about HECS-HELP

Centrelink support

The Australian Government offers a number of income-support payments to eligible Australian university students.

Learn about Centrelink payments for students

Scholarships

You may be eligible for more than 100 scholarships, including:

Applying online

All international applications should be submitted to UQ. If you prefer, you can use an  approved UQ agent in your country .

The program code for the Graduate Diploma in Computer Science is  5520 .

Find out more about applying for postgraduate coursework study

All domestic applications should be submitted to UQ.

The program code for the Graduate Diploma in Computer Science is 5520 .

Important dates

The closing date for this program is:

• To commence study in semester 2 - May 31 of the year of commencement.
• To commence study in semester 1 - November 30 of the previous year.

To learn more about UQ dates, including semester start dates, view the Academic Calendar .

• To commence study in Semester 1 - January 31 of the year of commencement.
• To commence study in Semester 2 - June 30 of the year of commencement.

Aboriginal and Torres Strait Islander applicants

For support with applying – or if you have any questions about university life – get in touch with our Aboriginal and Torres Strait Islander Studies Unit.

Contact the ATSIS Unit

Explore other programs

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Aalto Doctoral Programme in Science

Application period:

Language of instruction:, eligibility:, field of study:, organising school:, table of contents, choose doctoral studies at the school of science.

The research at our school focuses on advanced energy solutions, condensed matter and materials physics; creation and transformation of technology based business; data science and artificial intelligence; health technology; neuroscience; and software engineering.

Through our internationally-acclaimed high-level research we aim to make a significant impact on society.

Anna Cichonska uses data science to develop better healthcare

Dr. Cichonska has received two awards for her dissertation and now she helps develop preventive medicine using data science

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Doctoral education pilot

We are hiring 178 new doctoral researchers - get your doctorate from Aalto

Your path to doctoral studies at the School of Science

• Apply for a salaried doctoral researcher position at Aalto. Salaried doctoral researcher positions are advertised at  Aalto University Open positions .
• If you are interested in  full-time studies but there are no open positions at the moment, please directly contact a professor in charge of your intended research field to discuss the possible supervision of doctoral studies. Professors may also have information on other funding possibilities (such as grants), as well as on upcoming positions that are not yet announced.
• If you are employed elsewhere but wish to pursue doctoral studies, that is possible either as a  full-time or part-time student , depending on your situation. In this case, please contact the potential supervising professor directly. Note that to pursue the degree, you need to reside in Finland at least part of the study time.
• In some cases it is also possible to start pursuing doctoral studies without funding (part-time doctoral studies). In this case, please contact the potential supervising professor directly. Note that to pursue the degree, you need to reside in Finland at least part of the study time. Non-Finnish citizens need therefore to take into account  the income requirements of the Finnish Immigration Service. 
• After agreeing with the supervising professor, apply for the study right in our doctoral programme (in addition to a possible working contract at Aalto) - see instructions below. Please note that you are not a doctoral student before you are officially admitted to the doctoral programme, even if you already have a salaried doctoral research position.

Note: Before applying for the salaried positions and/or contacting the potential supervising professor(s), please check below the requirements and qualifications needed for applying for the study right in our doctoral programme.

Discover your research topic and find a supervising professor

Research activities at the departments are arranged under research groups. If your research interest aligns with one of the research groups, it will be easier to find a doctoral study place.

Find research groups under our Department pages Research fields and supervising professors in the School of Science

In addition to the departments' and research groups' pages, you can use Aalto University's research portal for finding the professors who are researching the area in which you want to do your doctoral research. Before applying for doctoral studies you should be aware of the research done and researchers working in the field of your interest.

Aalto University's research portal for finding our researchers and research projects

More information on discovering your research topic and finding a professor

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Basic information on doctoral studies

Objective of studies.

Doctoral students will be equipped with the professional skills and knowledge required for demanding academic research and teaching positions. They will be trained for management, research and development, and other specialist positions of the information society.

The departments, which are in charge of the doctoral programme’s education and research at the School of Science , offer research fields that are based on strong research traditions and research at the highest international level. The School of Science is also very active in various national and international doctoral education networks.

Content of studies and degree structure

Doctoral education at the School of Science is based on vigorous basic research, forming a strong basis for teaching as well as development and innovation activities. The most important part of the education is the research work, which is conducted in a robust and dynamic research environment. The theoretical studies that support the research work are tailored individually to suit the different needs of each doctoral student. The theses of the School of Science are of very high quality and often contain articles published in international peer-review journals. Full-time doctoral students can complete the doctoral degree in four years.

The Doctor of Science (Technology) degree consists of

• an approved doctoral thesis
• research field studies (20-35 ECTS)
• general research studies (5-20 ECTS)

Full-time or part-time studies

All doctoral students are defined as either full-time or part-time students. Doctoral students may not change their study mode by themselves, but it can be changed by application if necessary.

Full-time doctoral students plan their studies with the view to allowing the doctorate to be earned within four years of the right to pursue a doctoral degree being granted. Those applying for a full-time right to pursue a doctoral degree must have funding secured for at least 6 months (e.g. from the employer, or through project funding or a grant). Students employed outside Aalto University must append in their application a certificate issued by the employer proving their possibility to pursue full-time studies (i.e. a minimum of 80% of working hours may be used towards doctoral studies).

Those who do not meet the above criteria for full-time doctoral students are regarded as part-time doctoral students . This category includes, for instance, doctoral students who have a main occupation elsewhere than at the School of Science. Part-time doctoral students plan their studies so as to allow the doctoral degree to be completed in a maximum of eight years.

It is advisable to have a discussion with the part-time students on whether their research is closely connected to their work, and how their employer can support intensive doctoral studies alongside work.

Study language

The language of instruction is, in principle, English, but the doctoral thesis can also be completed in Finnish or Swedish.

The applicants will define in their application in which language they will pursue the degree. The possible languages are Finnish, Swedish, or English. If doctoral students want to write their doctoral thesis in Finnish or in Swedish, the language of the degree will be Finnish or Swedish. If the doctoral thesis is written in English, doctoral students can choose English as the language of the degree that must be approved by the doctoral programme committee. The language of degree can, on reasonable grounds, be changed at a later stage if the language of the doctoral thesis changes.

Funding and fees

The most common ways to fund doctoral studies are

• working in a salaried doctoral researcher position
• personal grant(s) or scholarship(s) from foundations or funding agencies

Most Aalto doctoral students combine different funding sources during their studies. 

How to find and apply for funding for doctoral studies and research? Generally Aalto University does not offer scholarships for doctoral studies. However, there is an on-going scholarship scheme until 2024 for students from African and South American countries: Finland Fellowship .

During the academic year 2024-2025, the Finnish Ministry of Education and Culture is funding 178 new doctoral researcher positions at Aalto University through a doctoral education pilot.

Doctoral education pilot: 178 doctoral researchers' salaried positions

Fees and costs

Aalto University doctoral studies are free of tuition fees. Aalto University does not charge fees for enrollment to the University. Doctoral students are welcome to join the Aalto University Student Union. The membership of the Student Union is subject to an annual fee.

If you need a residence permit for research in Finland, please see more information about income requirement at the Finnish Immigration Services .

International opportunities and Cotutelle

Internationality is an integral part of the school’s doctoral education in both recruiting and educating doctoral students. The School of Science is well connected to a number of top-level international universities and research institutes in its field.

Aalto University encourages its doctoral students to spend at least six months of their study time abroad, as international mobility enhances doctoral students’ career opportunities . Visiting a foreign university or research institute often means sharing knowledge and know-how, creating new ideas, expanding international networks, and developing one’s professional skills.

Cotutelle - joint supervision

The Aalto University encourages doctoral degrees that are jointly supervised with an international partner. These agreements are referred to as Cotutelle Agreements. Arrangements for joint supervision are made in terms of the studies and supervision of a single doctoral student that will be awarded degrees and the associated certificates from both universities.

Cotutelle agreements are part of the university's aim to achieve and maintain high quality of international standards in research and education. International cooperation is an integral part of the doctoral education of Aalto University, and one method for such cooperation is arrangements for the joint supervision of doctoral degrees. A doctoral student may earn a double degree under a joint supervision arrangement between two universities provided that the joint supervision is based on genuine scientific cooperation, bringing added value to the doctoral thesis and enhancing the quality of the research.

Cotutelle agreements should be made at the beginning of the studies but it requires a right to study at the Aalto University before any agreement can be made.

Multidisciplinary opportunities

The doctoral education networks collaborate through seminars, courses, summer schools and events as well as promoting networking, and increasing peer support among doctoral students.

Doctoral education networks

The School of Science operates in the following doctoral education networks:

Brain & Mind CMMP- Network in Condensed Matter and Materials Physics Doctoral Education Network in Systems Analysis, Decision Making and Risk Management ENNUSTE - Doctoral Education Network in Nuclear Science and Technology FDNSS - Finnish Doctoral Education Network in Stochastics and Statistics HICT - Helsinki doctoral education network in information and communications technology Nordic IoT Hub - Nordic collaboration in Industrial IoT

More information

Career opportunities and employability for Aalto doctoral students

Why choose Aalto University?

How to apply?

The Doctoral Programme in Science invites applications continuously (applications will not be processed in July). The more exact application times can be found below.

Before submitting the application, each applicant must contact a supervising professor who is responsible in their intended research field to doctoral studies and the supervision of the studies. Applicants are urged to ensure that their expertise and research interests are commensurate with the research group (and those of the supervising professor of their studies) that they apply to.

Application dates & deadlines

Applies to all applicants, with the exception of applicants for part-time doctoral studies in the research field of Industrial Engineering and Management .

Applicants to the research field of Industrial Engineering and Management

• Applications are invited from full-time applicants to the research field of Industrial Engineering and Management once a month. The application deadlines are identical with those of other research fields in the programme, please see deadlines above.
• Applicants to part-time doctoral studies in the field of Industrial Engineering and Management may apply twice a year.

Application periods 2024: part-time applicants, Industrial Engineering and Management

Processing the applications

Each application is processed in the next possible meeting of the Doctoral Programme Committee when all the required materials have been received. Only applications with all the required materials will be processed. Incomplete applications will be rejected, unless the missing materials are sent by the deadline given. The right to pursue a degree will be valid only after the dean has granted the right and the applicant has accepted the offer of admission. The applicant has two weeks to accept the offer and cannot postpone the start of the right to pursue a degree.*

*Please note:

From 1 August 2016 onwards applicants who have been given an offer of admission may accept only one student place leading to a higher education degree in Finland during one academic term (Universities Act 558/2009). Higher education degrees include bachelor’s, master’s, and doctoral degrees awarded by universities as well as degrees from universities of applied sciences.

The academic terms run from 1 August to 31 December and from 1 January to 31 July. The acceptance of a student place is binding and cannot be cancelled. Even if the accepted applicants postpone the commencement of studies or give up their right to study, they cannot accept another study place leading to a higher education degree starting the same academic term.

Eligibility

The general eligibility criteria for higher education are defined in the Universities Act (558/2009, section 37) and in the Aalto University Student Admissions Criteria for 2023.

Eligible applicants for studies leading to a doctoral degree from the Doctoral Programme in Science will have completed:

• a relevant master’s degree awarded by a university;
• a relevant master’s degree awarded by a university of applied sciences; or
• a relevant study programme abroad which in the awarding country gives eligibility for the corresponding level of higher education.

The university may require a student admitted to study towards an academic or artistic licentiate or doctoral degree to complete supplementary studies in order to acquire the knowledge and skills needed for the study programme.

Policy on the recognition of degrees awarded outside Finland

Applicants holding a degree earned abroad are eligible for doctoral studies provided their degree gives them eligibility for corresponding higher education in the awarding country. As a general rule, the grounds for Aalto’s recognition of degrees earned abroad is that the normative time to attaining them is at least four years, which includes a master’s thesis or the equivalent, and that such studies in the view of the school equip the student with the skills and knowledge needed to pursue doctoral studies at the school. The general policy on the recognition of a European degree as the basis for doctoral degree studies is that it be a higher-education degree combination (3+2 years) earned in accordance with the Bologna Process principles.

Eligibility on the grounds of higher-education degrees with a structure differing from the abovementioned are considered on a case-by-case basis. In such cases, attention will focus particularly on how well the degree has prepared the applicant to pursue doctoral studies.

Policy on the recognition of master’s degrees from Finnish universities of applied sciences

Applicants with a master’s degree in a relevant field earned at a Finnish university of applied sciences are reviewed on a case-by-case basis for their aptitude to pursue doctoral studies. If the school deems the applicant with a relevant master's degree from a university of applied sciences as having the potential to complete the doctoral degree, the applicant will be assigned sufficient supplementary studies to allow him or her to begin the doctoral/licentiate studies. The scope of the supplementary studies may not, however, exceed 60 ECTS credits. If the scope of the supplementary studies exceeds this maximum, the applicant is advised to complete a master's degree at the university level before applying for a right to pursue the doctoral degree.

When assessing the students’ potential for successful doctoral studies and determining their need for supplementary studies, the school will consider the scope of master’s degrees at the student’s university of applied sciences (in Finland, such degrees are of 60–90 cr, less than the 120-credit scope of master’s degrees at Finnish universities such as Aalto).

Applicants with a prior degree earned abroad and corresponding to a relevant Finnish master’s degree from a university of applied sciences are treated equally to those with master’s degree from a Finnish university of applied sciences.

Evaluation criteria

The academic evaluation of the applicant is performed by the supervising professor in charge of the research field sought by the applicant. The Doctoral Programme Committee also evaluates the applicants. In addition, all applicants to the field of industrial engineering and management are evaluated by the Department of Industrial Engineering and Management.

The evaluation of applicants takes into account the following criteria:

Contents of the previous degree(s):

• sufficient basic information concerning the research field applied for; this information is collected from e.g. the major studies or advanced studies that were included in the applicant’s master's degree
• sufficient basic information to support the applicant’s ability to carry out the research work and writing for the doctoral thesis

The School of Science may set prerequisite studies for the applicant to complete by e.g. taking master’s level courses.

Academic performance:

• The applicant received a minimum grade of at least 4 (on a scale of 1–5) for the master's thesis
• an average grade of 3.5 (on a scale of 1–5)  for the master’s degree courses (excluding the thesis), OR
• if the applicant earned his/her master’s degree in accordance with the degree regulations of 1995 or earlier (when Finnish bachelor's and master's degree were not pursued separately), he or she received an average grade of at least 3.0 (scale 1–5) for the major at the master's level.

Exceptions to the grade limits mentioned above may be made only for special reasons set forth in a well-founded written statement by the supervising professor. In such cases, the Doctoral Programme will request a statement from the supervising professor.

Other evaluation criteria:

• the quality of the applicant’s university degree and its status internationally
• the applicant’s potential as a researcher; previous experience in research research-related work experience, conference presentations, journal articles, etc.
• relevance of the research topic: to ensure genuine research interest by the supervising professor and adequate resources to advise the student in the research of the department’s focus area
• the research proposal: the theoretical and practical innovative value of the topic; the feasibility of the research proposal (its quality, planned methods, etc.)
• time management and resources: the research proposal and the time the student has available for the doctoral studies over the next four (4) years
• the supervising resources of the university in the research field
• other grounds for admission presented by the applicant

Applicant evaluations for the research field of industrial engineering and management:

The Department of Industrial Engineering and Management and the supervising professor are responsible for evaluating applicants to the research field of industrial engineering and management. The Doctoral Programme will request an evaluation statement from the department. Preference will be given to applicants who have shown outstanding academic performance and are otherwise strongly qualified for doctoral studies in the department.

Required language proficiency

Successful applicants will have excellent skills in Finnish, Swedish, or English. The applicant chooses the language(s) of instruction that he/she will use for demonstrating proficiency.

Proficiency in a national language of Finland is demonstrated in accordance with the ‘General recommendations for admissions criteria for Finnish universities’.

Proficiency in English may be demonstrated in one of the following ways:

• The applicant has earned a degree taught in Finnish, Swedish or English in a higher education institution in Finland;
• The applicant has earned an English-medium higher education degree in an EU/EEA country, Australia, Canada, New Zealand, South Africa, Switzerland, the United Kingdom or the United States while residing in the respective country;
• The applicant has received his or her primary and secondary education in English in an EU/EEA country, Australia, Canada, New Zealand, South Africa, Switzerland, the United Kingdom or the United States while residing in the respective country;
• The applicant has completed the Aalto Executive Education MBA, EMBA or DBA degree; 
• The applicant has completed CEMS Master in International Management entity; or
• Applicants submit their English-language test results in accordance with the information below.

In points 1–3 of the above list, a minimum of one half of the aforementioned degree must be completed in a country and higher education institution that meets the requirements for exempting the student from taking an English test. The language of the degree must be stated unambiguously in the degree certificate or its appendix, or in the transcript of records or other official document issued by the awarding institution. If the degree was completed in more than one language, the appendix must clearly indicate the amount of studies that were completed in English.

In points 1 – 2 of the above list, a university degree showing language proficiency must be at least three years' lower university degree, at least one year's higher university degree or doctorate. The university must be a recognised part of the country's official national education system. The university must be found on the country's official list of recognised universities with a right to degree-granting or on a list of recognised universities maintained by an international organisation (e.g., UNESCO). 

The university must be recognised in the country of point 1 or 2.

The recognized English language tests and their minimum scores required for admission to doctoral studies are (Aalto University Admissions Criteria 2022):

• IELTS (Academic): 6.5, and 5.5 for Writing;
• IBT (Internet-based Test): 92, and 22 for the Writing section or
• iBT® Special Home Edition: 92 and 22 for the Writing section
• PDT (Paper-Delivered Test): Reading 22, Listening 22, and Writing 24
• C1 Advanced, prev. Cambridge Certificate in Advanced English: A,B or C
• C2 Proficiency, prev. Cambridge Certificate of Proficiency in English: A,B,C or Level C1
• Pearson Test of English Academic (PTE A): 62, and 54 for writing
• When it comes to the IELTS and TOEFL tests, the following online tests are also accepted: TOEFL iBT® Special Home Edition ja IELTS Indicator (Academic).

In addition, at the School of Science, an English language test is not required in doctoral admissions of applicants who have:

• been deemed by their supervising professor as having sufficient proficiency in English to pursue doctoral studies. The supervising professor must submit a written statement on the language skills of the applicant and the statement should be added in the application. If the applicant does not submit the statement together with other application documents the Doctoral Programme will request the statement from the supervising professor.

Submission of English test result:

Only official reports of the language test are acceptable as proof of proficiency.

• For IELTS test, upload a copy of the official test report (as a PDF) to the application system. The test results are verified through the test administrator’s electronic verification service.
• TOEFL test results must be sent to Aalto University by the test administrator directly. Request your official score report to be sent from the test administrator to Aalto University reporting code  7364 . Unofficial score reports sent by applicants will not be accepted. Test scores submitted with the Aalto University reporting code are checked for authenticity in the test administrator’s verification database. The scores are available within 1 to 2 weeks of sending the score report request.
• PTE test scores are sent via the test administrator’s electronic service. Applicants who took the PTE test must log in to their PTE account and send their results in the system to Aalto University. The test administrator notifies by email when the results have been sent to the university.
• For the C1 Advanced or C2 Proficiency language test, upload a PDF copy of the results report to the application system. The test-taker’s ID number (e.g. ABC1234567) and reference number (e.g. 173YU0034522) must be entered in the system. Test takers must be logged in to the language test system to submit their results to Aalto University. The results are verified electronically.

Validity of the language test

The language test result has to be valid at least on the day of the Doctoral Programme Committee meeting where the applicant's application is processed.

More information on the language tests can be found on the pages of the test providers:

• IELTS  (ielts.org)
• TOEFL  (ets.org)
• PTE Academic  (pearsonpte.com)
• C1 Advanced (cambridgeesol.org)
• C2 Proficiency (cambridgeesol.org)

Application form and its appendices

• Before submitting the application the applicant must contact a professor (supervising professor) in charge of the intended research field to discuss starting doctoral studies
• Read the application instructions carefully and prepare all the needed appendices
• Register in the online application system (Studyinfo.fi), fill in the application, attach all the required documents and send it.

Electronic application form   (Studyinfo.fi)

In the application form, please choose Doctoral Programme in Science as a study programme ("Add study programme").

After reading the instructions and getting all the documents ready, register on the online application system, fill in the application, attach all the required documents and send it.

Required appendices (scanned in pdf form) for all applicants:

• An official copy of Master's degree certificate (does not apply to applicants with a master´s degree earned at Aalto University or the forerunners of Aalto University)
• An official transcript of records
• Curriculum vitae (CV) including a list of publications and a proof of other scientific activity
• Research plan  with an implementation schedule
• A preliminary Study plan (credit plan) for the theoretical studies of the degree , 40 ECTS
• Supervision plan (at SCI a deputy supervising professor has to be assigned always. The deputy professor has to be a tenure-track professor of the school)
• The source, the amount and the start and end dates of secured funding, More information on funding your doctoral studies (aalto.fi)
• Copy of the identity page of the passport or other official identification that indicates the citizenship of the applicant
• Proof of language proficiency (if needed, more instructions above and in the application form)

The study plan, research plan and supervision plan must all be approved and signed by both the applicant and the supervising professor. Upon his or her signature of the documents, the professor in charge of the research field agrees to act as the supervising professor of the applicant.  

Applicants with a master´s degree or education earned at an institution other than Aalto University (or the forerunners of Aalto University) shall also add the following documents in the electronic application form:

• Master's degree certificate
• Official transcripts of records of all the courses included in the master’s degree
• All appendices of the master's degree diploma, including the Diploma Supplement provided by international universities in European countries
• Official translations of the above, if the originals are not in Finnish, Swedish, or English.
• You must provide certified copies for all of the above-mentioned before you can be granted a right to pursue a degree. An exception to this are degrees completed in Finland after 1 January 2003, which are authenticated electronically with the help of a national database.
• An abstract of master's thesis in English
• A document confirming eligibility for doctoral studies , if needed (please see Eligibility for doctoral studies ) with an official translation, if the original document is not in Finnish, Swedish or English

*Degrees awarded by a higher education institution outside Finland:

Send certified hard copies of the required degree documents including official translations (listed above) also by postal mail to our office (Kitta Peura, Doctoral Programme in Science, P O BOX 15500, 00076 AALTO).

Country specific instructions:

Applicants who have completed their master's degree in: Australia, Bangladesh, Cameroon, Canada, China, Eritrea, Ethiopia, Ghana, India, Indonesia, Iran, Ireland, Kenya, Malaysia, Nepal, New Zealand, Nigeria, Pakistan, South Africa, Sudan, the United Kingdom or the United States:

Please note that the required degree documents including official translations need to be submitted according to the country-specific document requirements . Read the requirements on Country-specific requirements , and send the documents to the doctoral programme as required in the "Country specific requirements". If the documents need to be sent by postal mail, the address is: Kitta Peura, Doctoral Programme in Science, P O BOX 15500, 00076 AALTO. If you use courier services, please send the documents to this address: Kitta Peura, Doctoral Programme in Science, Maarintie 8, 02150 Espoo, Finland. If the documents can be submitted electronically (they can be verified in an electronic system), send them by e-mail to [email protected] . Please DO NOT send the documents to the Admission Services as instructed in the country specific requirements webpage.

*Degrees awarded by a higher education institution in Finland:

• If you have completed a degree in Finland after 1 January 2003 and you have a Finnish personal identification code , Aalto University will verify your degree electronically using a national database and you do not need to send anything by postal mail.
• If your degree was completed before 1 January 2003 , or you do not have a Finnish personal identification code, send certified hard copies of the required degree documents (listed above) to our office by postal mail (Kitta Peura, Doctoral Programme in Science, P O BOX 15500, 00076 AALTO).

Certified copies and translations

Copies of degree certificates and a transcript of records must be certified by the awarding university or by a notary public . The copies must be taken from the original, official documents. A multiple-page certified copy must be certified on the front side of every page. Each page must have the certifying official’s original signature, printed name, ink stamp and date . Copies of officially certified copies are not accepted, thecertifying official’s ink stamps and signatures must be original. A note declaring official copy status (such as a “True copy” stamp) is insufficient.

The translation is official if it has been done by the higher education institution that awarded the degree or by a certified translator (authorised translator) . The translations must have the certified translator’s original ink stamp and signature.

The official translations must be either original or certified paper copies of the original documents. Unofficial copies of the translations are insufficient. The official translations must be accompanied by certified paper copies of the original documents in the original language. Translations by themselves are insufficient.

Sending documents like certified copies by mail to Aalto University (guidelines given by the Finnish customs):

• Send the consignment of documents as a letter. Do not send it as a parcel.
• Do not determine a value for the letter, when sending it. Do not indicate even the amount paid for a possible insurance for the consignment.  
• If the country of dispatch requires the sender to determine a value for the consignment of documents, the value is zero (euros or other currency).
• The goods description for the consignment should be, for example, documents .

If you have any questions about admissions and application instructions, you can contact [email protected]

In case of programme-specific questions and inquiries, please contact the Learning Services of the Doctoral Programme in Science .

Departments of School of Science

Department of mathematics and systems analysis.

Our main research areas are algebra and discrete mathematics, analysis, applied mathematics and mechanics, stochastics and statistics, and systems analysis and operations research.

Department of Computer Science

To foster future science and society.

Department of Industrial Engineering and Management

We conduct world-class research and education focusing on the creation and transformation of technology-based business.

Department of Neuroscience and Biomedical Engineering

We study system-level dynamic functions of the human brain, mind and body.

Department of Applied Physics

The Department of Applied Physics pursues vigorous research in physical sciences and creates important industrial applications.

• Published: 19.12.2019
• Updated: 28.3.2024

• Equity & Inclusion

U.S. News & World Report ranks UC Berkeley computer science graduate program No. 1

UC Berkeley’s computer science graduate program was ranked first in the nation for the second year in a row by U.S. News & World Report , according to 2024 rankings  released April 8.

Berkeley’s program in the Department of Electrical Engineering and Computer Sciences shared the top spot with computer science programs at the Massachusetts Institute of Technology, Stanford University and Carnegie Mellon University. 

Several other Berkeley graduate programs in business, public health, public affairs and more were listed in the top 20 for their disciplines. These rankings are based on a survey of academics at peer institutions, according to U.S. News .

Berkeley’s Department of Electrical Engineering and Computer Sciences is shared by the College of Computing, Data Science, and Society and the College of Engineering. Learn more about Berkeley’s computer science graduate program.

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• News & Events

PhD student receives fellowship from Apple Scholars program

Nataliya Nechyporenko, a computer science Ph.D. student, has received a PhD fellowship in AI and Machine Learning (AIML) through the Apple Scholars program . The program was created by Apple to recognize the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. 

The fellowship provides Nechyporenko support for her research and academic travel for two years, internship opportunities and a two-year mentorship with an Apple researcher. 

Let's learn more about Nechyporenko's research aims and her perspective on the future of robotics research: 

What research do you hope to accomplish through this fellowship?

Think about how you might manually feel around an object to understand its shape, weight, and texture. Or if something is in your way, you'd just push it aside without overthinking it. If you drop something, you'll persistently keep trying to pick it up from different angles until you get it. As you're doing these everyday tasks, you're constantly building up an intuitive sense of your surroundings through trial-and-error. That's the kind of resourceful, flexible, multi-sensory approach I want robots to have when manipulating things – rather than just blindly following a fixed routine. 

The goal is for robotic arms to move and behave with that same kind of curious, improvisational, problem-solving spirit we take for granted as humans. As an Apple AIML scholar, I hope to gain insights into this problem with the help of a fresh network of mentors and collaborators.  

Is this an extension of work you are already doing in your lab? If so, how?

Driven to establish contact-rich planning as a dominant feature in robotics, I focused the first two years of my PhD on analyzing the methods used by state-of-the-art planners and solving the shortcomings leading to the lack of physical robot interaction. 

I have started to extend this work by integrating the empirical formulation of machine learning with model-based algorithmic approaches. I believe this is the path to making robots more adaptable to chaotic human environments. I will continue this work as an Apple scholar. 

What do you think of the current hype around AI and ML? What do you wish people understood about this research area?

The AI and machine learning hype trains have been barreling full steam ahead lately. But robotics? That's an entirely different beast that doesn't follow the overnight disruption narratives. It's a synergy of achievements in areas like materials, manufacturing, sensing, controls theory, and others aligning to reshape the physical world. 

The robotics future will reshape industries and labor concepts, but it will be catalyzed through the patient advancement of many disciplines.

How did you come to study at CU Boulder?

I spent a couple years in the trenches, getting my hands dirty actually building and deploying robots in industry. But after a while, I got this craving -- like there was so much more potential waiting to be unlocked if I could really dive into the deep scientific questions around robotics. That's why I decided to take the plunge back into academia.

What is one of your plans or hopes for the future, either professionally or personally?

I hope to be an expert, a leader, a thinker and a builder. Outside of research endeavors, I aim to be a leader and educator for the robotics and the AI community. Previously, I’ve led volunteering activities, mentored students, and co-organized events that foster discussions around AI. I hope to continue to do so in the future at a larger scale. 

• Alessandro Roncone
• Graduate Student Stories

Nataliya Nechyporenko

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Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

At a Glance

Start Date May 2024

Application Deadlines Rolling Admissions

We are still accepting applications for a limited number of remaining spots to start in Summer 2024. Apply today to secure your space in the program.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.