data science phd programs europe

Best Universities for Data Science in Europe

Updated: February 29, 2024

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Below is a list of best universities in Europe ranked based on their research performance in Data Science. A graph of 8.92M citations received by 333K academic papers made by 727 universities in Europe was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. University College London

For Data Science

2. University of Oxford

3. Imperial College London

4. University of Bristol

5. University of Manchester

6. University of Cambridge

7. University of Edinburgh

8. University of Southampton

9. University of Birmingham

10. King's College London

11. St George's, University of London

12. University of Sheffield

13. Catholic University of Leuven

14. University of York

15. University of Amsterdam

16. University of Glasgow

17. University of Liverpool

18. Swiss Federal Institute of Technology Zurich

19. University of London

20. Newcastle University

21. Leiden University

22. Delft University of Technology

23. University of Nottingham

24. University of Leeds

25. Federal Institute of Technology Lausanne

26. University of Zurich

27. Eindhoven University of Technology

28. University of Warwick

29. University of Leicester

30. Polytechnic University of Milan

31. Cardiff University

32. Technical University of Munich

33. University of Granada

34. Utrecht University

35. Uppsala University

36. Radboud University

37. Queen Mary University of London

38. University of Exeter

39. Technical University of Madrid

40. University of Bologna

41. Claude Bernard University Lyon 1

42. Vienna University of Technology

43. University of Copenhagen

44. Heidelberg University - Germany

45. Free University Amsterdam

46. Ghent University

47. University of Aberdeen

48. Technical University of Catalonia

49. University of Helsinki

50. University of Geneva

51. University of Pisa

52. RWTH Aachen University

53. University College Dublin

54. Sapienza University of Rome

55. Maastricht University

56. KTH Royal Institute of Technology

57. Lancaster University

58. Federico II University of Naples

59. Karlsruhe Institute of Technology

60. Polytechnic University of Valencia

61. University of Twente

62. Erasmus University Rotterdam

63. Norwegian University of Science and Technology

64. University of Bern

65. University of Stuttgart

66. London School of Economics and Political Science

67. University of Groningen

68. Polytechnic University of Bari

69. Aalto University

70. University of Porto

71. Technical University of Berlin

72. Ulster University

73. University of Wales

74. University of Patras

75. University of Oslo

76. Pierre and Marie Curie University

77. University of Dundee

78. Swansea University

79. Keele University

80. Aarhus University

81. National Technical University of Athens

82. Durham University

83. Lund University

84. University of Munich

85. City, University of London

86. Queen's University Belfast

87. Aalborg University

88. University of Surrey

89. University of Sussex

90. University of Vienna

91. University of Milan

92. University of Reading

93. Dresden University of Technology

94. Brunel University London

95. Wageningen University

96. University of East Anglia

97. Technical University of Denmark

98. University of Leipzig

99. Karolinska Institute

100. University of Padua

Computer Science subfields in Europe

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• Munich Data Science Institute
• Technical University of Munich

Doctoral Studies

For PhD students at TUM whose research projects are related to data science issues, MDSI offers various funding and qualification opportunities.

• Application
• Data Science Colloquia

What is Data Science?

Data Science is emerging as a disruptive consequence of the digital revolution . Based on the combination of big data availability, sophisticated data analysis techniques, and scalable computing infrastructures , Data Science is rapidly changing the way we do business, socialize, conduct research, and govern society. It is also changing the way scientific research is performed. Model-driven approaches are supplemented with data-driven approaches. A new paradigm emerged, where theories and models and the bottom up discovery of knowledge from data mutually support each other. Experiments and analyses over massive datasets are functional not only to the validation of existing theories and models, but also to the data-driven discovery of patterns emerging from data, which can help scientists design better theories and models, yielding deeper understanding of the complexity of social, economic, biological, technological, cultural and natural phenomena.

Data science is an interdisciplinary and pervasive paradigm aiming to turn data into knowledge, born at the intersection of a diversity of scientific and technological fields: databases and data mining , machine learning and artificial intelligence , complex systems and network science , statistics and statistical physics , information retrieval and text mining , natural language understanding, applied mathematics . Spectacular advances are occurring in data-driven pattern discovery, in automated learning of predictive models and in the analysis of complex networks.

Within this context, the Ph.D. in Data Science is aimed at educating the new generation of researchers that combine their disciplinary competences with those of a “data scientist”, able to exploit data and models for advancing knowledge in their own disciplines, or across diverse disciplines. To this purpose, the Ph.D. in Data Science develops a mix of knowledge and skills on the methods and technologies for the management of large, heterogeneous and complex data, for data sensing (how to harvest data), for data analysis and mining (how to make sense of data), for data visualization and storytelling (how to narrate data), for understanding the ethical issues and the social impact of Data Science. The Ph.D. students will have the opportunity of developing data science projects in a variety of domains, including:

• Data science for society and policy
• Data science for economics and finance
• Data science for culture and the humanities
• Data science for industry and manufacturing
• Data science for biology and health
• Data science for the hard and environmental sciences
• Data science ethics and legal aspects
• Data science techniques and methods

Applications from graduate students from any discipline are welcome. The successful candidate is expected to possess a solid motivation and personal preparation, and a strong propensity towards quantitative studies in own field. More information on the Call for Application can be found on the dedicated pages of the Scuola Normale Superiore .

In Evidence

Academic year 2021-2022 (37th cycle).

As the Italian Ministry has decided to invest in a doctoral program on Artificial Intelligence, next year the Data Science Ph.D. will become one of the 5 nodes of a new national initiative: the National Artificial Intelligence Ph.D. All the partner institutions of the current program will join the new Ph.D.

The call for admissions to the National PhD in Artificial Intelligence is now open!

Interested in a multi-disciplinary PhD course oriented at cutting-edge research in human-centered Artificial Intelligence and its impacts on society? Apply to one of 44 fully-funded positions at the National PhD in AI – “Society” area:

https://dottorato.unipi.it/index.php/en/application-process-for-the-acad...

Deadline: July 23, 2021, h 13:00 CET

The program is launched by the University of Pisa in partnership with:

- National Research Council - CNR - Scuola Superiore Sant’Anna - Scuola Normale Superiore - Scuola IMT Lucca - Università di Firenze - Università di Modena e Reggio Emilia - Università di Siena - Università di Trento

and in collaboration with:

- Università di Bari - Università di Bologna - Università Cattolica del Sacro Cuore - Università dell’Aquila - Università degli Studi di Napoli L’Orientale - Università di Sassari - Università di Trieste - INDAM (Istituto Nazionale di Alta Matematica “Francesco Severi”) - Open Fiber SpA

This opportunity is part of the Italian National PhD Program in Artificial Intelligence. Overall, PhD-AI.it is made of 5 federated PhD courses that bring together 61 Italian universities and research institutions. The 5 PhD courses share a common basis in the foundations and developments of AI, and each one has an area of specialisation in a strategic sector of AI application. Each PhD course is organized by a lead university, in collaboration with the National Research Council CNR:

- Health and life sciences, Università Campus Bio-Medico di Roma - Agrifood and environment, Università degli Studi di Napoli Federico II - Security and cybersecurity, Sapienza Università di Roma - Industry 4.0, Politecnico di Torino - Society, Università di Pisa

Link to the calls for admissions to all the 5 PhD course are available at http://www.PhD-AI.it

    Language: English

    Approach: Multidisciplinary

    Lenght: 3 Years

    Venue: Pisa, Italy

    Students: 9 + 2

    Start: November 1st

    Requirements: Any Master Degree

    Max Age: Born after 31 Oct 1987

    Grant: 15012   /year

    Lodging: Included

    Meals: Included

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In 2024 UEL celebrates a Year of Science

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Data Science Prof Doc

This course is in clearing

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The Professional Doctorate in Data Science (D. DataSc) is aimed at professionals who wish to enhance and/or validate data-centric, evidence-based approaches within their chosen career through a combination of taught modules and doctoral research.

The programme is delivered:

• Full-time, three years: one year of taught modules and two years of research
• Part-time, five years:  two years of taught modules and three years of research

A cross-disciplinary approach is central to the delivery of this programme and is therefore suitable for professionals in a broad range of professional disciplines and areas of employment.

"The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that's going to be a hugely important skill in the next decades." (Hal Varian, Chief Economist at Google).

The programme is unique, international, and ground-breaking in offering a Professional Doctorate qualification in Data Science. D. DataSc is an earned doctorate that allows the holder to use the title 'Dr'.

This course is only eligible for part-time student visa sponsorship. For more details about the restrictions of part-time student visas please see our Student Visa page .

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Course options

• September 2024

Professional Doctorate

Entry requirements, academic requirements, accepted qualifications.

Bachelor's degree with Upper Second Class (2:1) in Physical Science, Electrical, Electronic, Communication Engineering or Humanities and Social Science related subject.

International Qualifications

We accept a wide range of European and international qualifications in addition to A-levels, the International Baccalaureate and BTEC qualifications. Please visit our International page for full details.

English Language requirements

Overall IELTS 6.5 with a minimum of 6.0 in Writing, Speaking, Reading and Listening (or recognised equivalent). If you do not meet the academic English language requirements for your course, you may be eligible to enrol onto a pre-sessional English course .

The length of the course will depend on your current level of English and the requirements for your degree programme. We offer a 5-week and an 10-week pre-sessional course.

Mature applicants and those without formal qualifications

As an inclusive university, we recognise those who have been out of education for some time may not have the formal qualifications usually required. We welcome applications from those who can demonstrate their enthusiasm and commitment to study and have the relevant life/work experience that equips them to succeed on the course. We will assess this from the information provided in your application or may request additional information such as a CV or attendance at an interview. Please note that some courses require applicants to meet the entry requirements outlined.

Admissions policy / Terms of Admittance

We are committed to fair admissions and access by recruiting students regardless of their social, cultural or economic background. Our admissions policy sets out the principles and procedures we use to admit new students for all courses offered by the university and its partners.

Further advice and guidance

You can speak to a member of our Applicant Enquiries team on +44 (0)20 8223 3333, Monday to Friday from 9am to 5pm. Alternatively, you can visit our Information, Advice and Guidance centre.

Prof Doc Data Science

Prof doc data science, home applicant, full time.

• Home Applicant
• Full time, 3 years
• 10200 first year fees £10,200 (taught element), then £6,020 per year for the next two research years. Pound 10200 first year fees £10,200 (taught element), then £6,020 per year for the next two research years.

Prof Doc Data Science, home applicant, part time

• Part time, 5 years
• 1700 first year fees £1,700 (taught element) per 30 credit module, then £3,010 per year for the next three research years. Pound 1700 first year fees £1,700 (taught element) per 30 credit module, then £3,010 per year for the next three research years.

Prof Doc Data Science, international applicant, full time

• International Applicant
• 15960 first year fees £15,960 (taught element), then £16,100 per year for the next two research years. Pound 15960 first year fees £15,960 (taught element), then £16,100 per year for the next two research years.

Prof Doc Data Science, international applicant, part time

• 2660 first year fees £2,660 (taught element) per 30 credit module, then £8,050 per year for the next three research years. Pound 2660 first year fees £2,660 (taught element) per 30 credit module, then £8,050 per year for the next three research years.

Fees, funding and additional costs

EU, EEA and Swiss Nationals starting a course from September 2021, will no longer be eligible for Home fees. However, such nationals benefitting from Settled Status or Citizens' Rights may become eligible for Home fees as and when the UK Government confirms any new fee regulations. Further information can be found at UKCISA .

Tuition fees are subject to annual change. Fees for future years will be published in due course.

Home students

Postgraduate loans scheme.

£10,280 to fund your Masters Programme under the Postgraduate Loans (PGL) scheme

Postgraduate Loans (PGL)

The Postgraduate Loan (PGL) provide non-means-tested loans of up to £10,906 to taught and research masters students.  It will be paid to students as a contribution towards tuition fees, living costs and other course costs. Applications are made directly through  Student Finance England  

Eligibility

Whether you qualify depends on: •    if you've studied a postgraduate course before •    your course •    your age •    your nationality or residency status

Full eligibility can be found on the Government's Postgraduate Loan webpage .

Please take a look at the  Postgraduate Loans  for an overview of the new funding.

Postgraduate Scholarship

Apply for a 50 per cent discount on your tuition fees! You can get a 50 per cent discount on course fees through a UEL Postgraduate Scholarship. The scholarship is open to full-time and part-time UK and EU students of taught postgraduate courses. *Exclusions apply.

Find out more about full eligibility criteria and how to apply .

Terms and conditions apply.

Our scholarships and bursaries can help you

How we can help you

Did you know that with a postgraduate qualification, you can expect to earn more than someone who only holds an undergraduate degree?

If you want to build new skills, change career paths, or further your career prospects, a postgraduate degree can help you. Our range of scholarships and bursaries will make financing your education that much easier. Below is some of the funding available to support you in your studies:

• Alumni Discount   - up to 15% fee waiver *exclusions apply. Please see the Alumni Discount page  for information.
• Early Payment Discount  - 5% fee waiver
• Asylum Seekers scholarship   - 100% fee waiver
• Civic Engagement - £1,000
• Hardship Bursary - up to £2,000
• Sport Scholarships   - Up to £6,000

How to pay your fees

There are a number of ways you can pay your fees to UEL

• Online payment facilities
• By telephone
• In person at our Docklands or Stratford campus
• Bank transfer

Full information on making payments can be found  on our Finance page .

If you wish to discuss payments to the University, please contact our Income Team on 020 8223 2974 or you can email  [email protected]

Ideas for funding your postgraduate study

Below are some ideas on how to fund your postgraduate study:

•     Apply for a  Postgraduate Loan  
•     Take advantage of  UEL scholarships and bursaries
•     Ask your employer to sponsor your study
•     Study part-time so you can work at the same time (applicable to courses that have a part-time mode)
•     Look at  UK Research and Innovation funding options

The Student Money Advice and Rights Team (SMART) are here to help you navigate your finances while you're a student at the University of East London. We can give you advice, information and guidance on government and university funds so that you receive your full funding entitlement. Live chat: Click the live chat icon in the bottom left of the screen Phone: 020 8223 4444

International students

Living costs for international students.

As part of the Tier 4 student visa requirements, UK Visas and Immigration (UKVI) estimate that you will need £1,265* per month to cover your living costs. It includes expenses for accommodation, food and drink, travel within London, textbooks, entertainment, clothing, toiletries and laundry. Most Tier 4 students are required to show they have sufficient funds to cover the first nine months of the course before they start - a total of £11,385 - in addition to the tuition fees. You can find more information about the specific requirements of the Tier 4 student visa. The amount that you will spend can vary depending on your lifestyle. The UKCISA International Student Calculator can help you plan and manage your money.

* Please note the Immigration Rules are subject to change and this figure is likely to be increased by UKVI year on year. Please therefore check our ISA page for more information at the time of preparing your visa application.

How to pay your fees - international students

Deposits and paying by instalments International students are required to pay a  deposit  before being issued a Confirmation of Acceptance for Studies (CAS). Your remaining balance will be paid in five monthly instalments over your first term. The first of these instalments must be paid when completing your enrolment on arrival at UEL. Please follow the payment instructions on our Make a Payment page . After the required payment has been made, you will be asked to complete the online International Student Reply Form to confirm your acceptance of our offer and of our terms of admittance and fee policy.

Our International team at UEL are available for advice and guidance on studying in London, fees, scholarships and visa requirements. Email:  [email protected]

Additional costs

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. Accommodation and living costs are not included in our fees. 

Our libraries are a valuable resource with an extensive collection of books and journals as well as first-class facilities and IT equipment. You may prefer to, or be required to, buy your own copy of key textbooks.

Computer equipment

There are open-access networked computers available across the University, plus laptops available to loan. You may find it useful to have your own PC, laptop or tablet which you can use around campus and in halls of residences.

Free WiFi is available on each of our campuses.

In the majority of cases, coursework can be submitted online. There may be instances when you will be required to submit work in a printed format. Printing and photocopying costs are not included in your tuition fees.

Travel costs are not included but we do have a free intersite bus service which links the campuses and halls of residence.

For this course, you will be:

• involved in processes of making, as a means of exploration, experimentation, and understanding your practice, by using a diverse range of media and materials
• required to purchase your own copy of books, for required reading
• required to produce physical artefacts for assessment 
• able to participate in optional study visits and/or field trips

However, over and above this you may incur extra costs associated with your studies, which you will need to plan for. 

To help you budget, the information below indicates what activities and materials are not covered by your tuition fees:

• personal laptops and other personal devices 
• personal copies of books 
• optional study visits and field trips (and any associated visa costs)
• printing costs
• your own chosen materials and equipment
• costs of participating in external events, exhibitions, performances etc.

The costs vary every year and with every student, according to the intentions for the type of work they wish to do. Attainment at assessment is not dependent upon the costs of materials chosen.

Learn about applying

Important information about your application, uk full-time starting sept.

How to apply Apply directly to UEL by clicking on the apply button. For further information read our  Guide to Applying . When to apply Places on many courses are limited and allocated on a first-come first-served basis. We advise you to apply as early as possible to give yourself the best chance of receiving an offer. Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone. +44 (0)20 8223 4354 Already applied? You can track the progress of your application by contacting our Applicant Engagement team on +44 (0)20 8223 3333 (Monday - Friday, 9am - 5pm). Read our  guide to applying  for further information. Need help? Contact our Applicant Engagement team (Monday - Friday, 9am - 5pm) +44 (0)20 8223 3333

UK Part-time starting Sept

How to apply Apply directly to UEL by clicking on the apply button. For further information read our  Guide to Applying . When to apply Places on many courses are limited and allocated on a first-come first-served basis. We advise you to apply as early as possible to give yourself the best chance of receiving an offer. Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone. +44 (0)20 8223 4354 Already applied? You can track the progress of your application by contacting our Applicant Engagement team on +44 (0)20 8223 3333 (Monday - Friday, 9am - 5pm). Read our  guide to applying  for further information. Need help? Contact our applicant engagement team (Monday - Friday, 9am - 5pm) +44 (0)20 8223 3333

International Full-time starting Sept

Submitting your application please read and consider the entry and visa requirements for this course before you submit your application. for more information please visit our  international student advice pages .  .

How to Apply We accept direct applications for international students. The easiest way to apply is directly to UEL by clicking on the red apply button. Please be sure to  watch our videos  on the application process.

When to Apply Please ensure that you refer to the international admissions deadline . We advise you to apply as early as possible to give yourself the best chance of receiving an offer.

International students who reside overseas Please ensure that you have read and considered the entry requirements for this course before you submit your application. Our enquiries team can provide advice if you are unsure if you are qualified for entry or have any other questions. Please be sure to read about the  Tier 4 visa requirements .

Advice and guidance Our  Information, Advice and Guidance team  provide impartial advice on courses, entry requirements, pre-entry and access programmes in person and via the telephone.

+44 (0)20 8223 4354 Need help? Contact our applicant engagement team (Monday - Friday, 9am - 5pm)

+44 (0)20 8223 3333

About our foundation years

Our Foundation Year courses are perfect for you if you... 

• are returning to education after a long time, or you don't have the qualifications for direct entry into our degree programmes
• are thinking of re-training and would like an introduction to the area
• are an international student wanting an additional year to adapt to the UK academic system
• are still evaluating which degree pathway at UEL is the right one for you

Please note: Foundation years can only be studied full-time. However, you can transfer to part-time delivery once you have completed your foundation year. Please apply to the full-time option if you wish to study in this way.

What makes this course different

Professional skill development

Block mode teaching, suitable for students in employment, allowing for professional skill development.

Enhanced knowledge

Integration of concepts, techniques and applications to enhance students' knowledge and skills in the analytics pipeline.

Open Source software tools

Open Source software tools which are widely used in the field of Data Science to extract value from data.

Course modules

Mental wealth; professional life (data ecology) core module.

This module aims to develop a critical understanding of the world of data and Data Science from an ‘ecological’ perspective. This will focus on an understanding the environment of production, dissemination, harvesting and use of data in the data value chain as well as the development of niche areas from a perspective of evolution, competition, life cycle, cross-fertilisation and the niche space. This module focuses on many aspects of working in an Industry 4.0 economy.

Research Methods for Technologists Core Module

Applied research tools and techniques core module, work-based project review core module, planning for doctoral research core module, advanced decision making: predictive analytics & machine learning optional module.

This module aims to develop a deep understanding of ways of making decisions that are based strongly on data and information. Particular focus will be on mathematical, statistical and algorithmic-based decision-making models using predictive analytics and machine learning. Various cases will be examined. The software environment will be predominantly open-source.

Spatial Data Analysis Optional Module

This module aims for students to understand the concept and theory of spatial data analysis, and develop the skill and problem-solving ability by applying a range of spatial query, processing, visualisation and analysis techniques. Main platforms with be open source SpatiaLite and QGIS.

NOTE: Modules are subject to change. For those studying part time courses the modules may vary.

Download course specification

PDF, 185.2kb

What we're researching

Data analysis, data mining and modelling, Geocomputation and mapping, and data management. Professor Brimicombe is Emeritus Professor at UEL. He is a Chartered Geographer, an Academician of the Academy of Social Sciences, a Fellow of the Royal Statistical Society, a fellow of the Royal Geographical Society, deputy chair of the National Statistician's Crime Statistics Advisory Committee and a non-executive committee member of the British Society of Criminology. He has been a Specialist Advisor to the House of Lords. Allan's expertise focuses on cross-disciplinary applications of Geo-Information Science and Data Science. Allan pioneered the use of geo-information systems and environmental simulation modelling. His other research interests include data quality issues, spatial data mining and analysis, predictive analytics and location-based services (LBS). These have been applied to crime, health, education, natural hazards, utilities and business. Allan's recent projects include Olympic Games Impact Studies and Smart City Studies. Dr Yang Li is a fellow of the Royal Geographical Society, a fellow of the Royal Statistical Society, a fellow of the Higher Education Academy and a member of the Association of Geographic Information. Yang has rich experiences in both applications and research of Data Science and Geo-Information Science. He has expertise in data integration, data mining and data modelling. Particularly, he is a specialist in geocomputational analysis including data quality modelling and sensitivity analysis. Yang's recent projects include Olympic Games Impact Studies, the Prevent Project of the Home Office and TURaS.

Your future career

This programme uniquely qualifies you in a field increasingly recognised as central to most professional areas and research. The research component provides a solid grounding in methods and engagement with leading-edge ideas. Job opportunities in data science are rising exponentially. Holders of a Professional Doctorate in Data Science will have the highest possible qualification in this area and prepare them for senior positions. They will also be eligible to apply for Royal Statistical Society membership.

Our students are professionals from a diverse range of areas. They include a global compliance engineer, a senior system analyst, an analytical chemist, an assistant dean at Qatar University, a SAP technology consultant from Germany, an IT trainer, a senior project manager with Diageo, an ICT manager from Ireland, a lecturer in databases from Oman, a principal consultant with Verizon, a company MD, a senior analytical consultant with TripAdvisor, a consultant with HSBC,  a software developer with HMRC, a school teacher, a marketing officer,  a data manager in Microsoft and a data analyst from New York. 

All are looking to improve their career options and general expertise in this expanding market.

Explore the different career options you can pursue with this degree and see the median salaries of the sector on our  Career Coach portal .

How we support your career ambitions

We offer dedicated careers support, and further opportunities to thrive, such as volunteering and industry networking. Our courses are created in collaboration with employers and industry to ensure they accurately reflect the real-life practices of your future career and provide you with the essential skills needed. You can focus on building interpersonal skills through group work and benefit from our investment in the latest cutting-edge technologies and facilities.

Career Zone

Our dedicated and award-winning team provide you with careers and employability resources, including:

• Online jobs board for internships, placements, graduate opportunities, and flexible part-time work.
• Mentoring programmes for insight with industry experts 
• 1-2-1 career coaching services 
• Careers workshops and employer events 
• Learning pathways to gain new skills and industry insight

Mental Wealth programme

Our Professional Fitness and Mental Wealth programme issues you with a Careers Passport to track the skills you’ve mastered. Some of these are externally validated by corporations like Amazon and Microsoft.

We are careers first

Our teaching methods and geographical location put us right up top

• Enterprise and entrepreneurship support 
• We are ranked 6th for graduate start-ups 
• Networking and visits to leading organisations 
• Support in starting a new business, freelancing and self-employment 
• London on our doorstep

What you'll learn

Our doctoral research course focuses on pure or applied aspects of data science, with each student studying data from within their main discipline or area of employment. You will learn reflective and analytic approaches to data while engaging in your own data research.

The taught elements of the course include Data Ecology, Research Methods for Technologists, Applied Research Tools and Techniques, Spatial Data Analysis, Advanced Decision Making, Work-based Project Reviews and Planning for Doctoral Research.

These elements will be reinforced by the specialist knowledge of our course leaders, whose fields of expertise include data cleansing, data integration, data mining, spatial analysis and predictive analytics.

Their recent research has engaged them in data from crime statistics, natural hazards, public health and business, keeping them at the forefront of new developments in the field.

Our cross-disciplinary approach to the subject means that whatever your area of interest, our researchers will have the experience and expertise to enhance your knowledge and skills.

The taught modules on this course are available to be taken as credit-bearing short courses by suitably qualified individuals.

How you'll learn

This programme includes six taught modules and a Research Thesis and is available in full-time and part-time modes. Delivery of taught modules is by block and blended learning.

For those studying full-time, there are two years of research and for those studying part-time,  it is two years of taught modules and three  years of research.

Each taught module is based on one week's intensive attendance at the Docklands campus, according to an advertised calendar, usually at the beginning of each semester. Students are expected to have a laptop computer for in-class practical sessions. During the remainder of the semester, students can work on their reading, practical components (from a workbook) and coursework. Students will be supported online or on campus depending on individual students' arrangements. The taught modules on this programme are available to be taken as credit-bearing short courses by suitably qualified individuals.

How you will be assessed

All the learning outcomes of the programme are assessed through:

• Laboratory session portfolios
• Research thesis

Campus and facilities

Our campus and the surrounding area.

Our waterfront campus in the historic Royal Docks provides a modern, well-equipped learning environment.

Join us and you'll be able to make the most of our facilities including contemporary lecture theatres and seminar rooms, art studios and exhibition spaces, audio and visual labs and a multimedia production centre.

Features include our 24/7 Docklands library, our £21m SportsDock centre, a campus shop and bookstore, the Children's Garden Nursery, cafés, eateries, a late bar, plus Student Union facilities, including a student lounge.   The University of East London is one of the few London universities to provide on-campus accommodation. Our Docklands Campus Student Village houses close to 1,200 students from around the world. We are well connected to central London and London City Airport is just across the water. We also run a free bus service that connects Docklands with Stratford campuses.

Who teaches this course

This course is delivered by the School of Architecture, Computing and Engineering.

The teaching team includes qualified academics, practitioners and industry experts as guest speakers. Full details of the academics will be provided in the student handbook and module guides.

Related courses

This course is part of the Computer Science and Digital Technologies subject area.

Prof Doc Information Security

This programme aims to develop research-based practice amongst professionals currently working within the Information Security area.

Architecture, Computing and Engineering MPhil PhD

ACE has strong research expertise in urban sustainability, cyber-security and big data studies. We're world leaders in environmental protection studies.

TERMS AND CONDITIONS Modal

Terms of Admittance to the University of East London

The Terms of Admittance govern your contractual relationship with the University of East London ("UEL"). A contract between you, the Student, and us, UEL, is entered into once you accept an offer of a place on a programme at UEL and this contract is subject to consumer protection legislation. You are entitled to cancel this contract within 14 days of enrolment onto your programme.

1) Student enrolment

Enrolment at UEL is the process whereby you officially become a UEL student. The enrolment process requires you to:

• Ensure that we are holding the correct personal details for you
• Agree to abide by our regulations and policies
• Pay your tuition fees/confirm who is paying your tuition fees

You are expected to enrol by the first day of your academic year (click on "Discover") which will be notified to you in your enrolment instructions. Failure to enrol by the deadline contained in our Fees Policy (for most students by the end of the second week of teaching) may lead to the cancellation of student status and all rights attached to that status, including attendance and use of UEL's facilities. If you do not complete the formal process of enrolment but, by your actions, are deemed to be undertaking activities compatible with the status of an enrolled student, UEL will formally enrol you and charge the relevant tuition fee. Such activities would include attendance in classes, use of online learning materials, submission of work and frequent use of a student ID card to gain access to university buildings and facilities. Late enrolment charges may be applied if you do not complete your enrolment by the relevant deadline.

2) Tuition fees

Your tuition fee is determined by:

• the programme you are studying;
• if you are studying full or part-time;
• whether you are a UK/EU or International student; and when you started your studies with us.

We will tell you the tuition fee that you are due to pay when we send you an offer as well as confirm any additional costs that will be incurred, such as bench fees or exceptional overseas study trips. Unregulated tuition fees (where the UK government has not set a maximum fee to be charged) are generally charged annually and may increase each year you are on the programme. Any annual increase will be limited to a maximum of 5% of the previous year's fee. Regulated tuition fees (where the UK government has set a maximum fee to be charged) may also be subject to an annual increase. Any annual increase will be in line with the increase determined by the UK government. You will be notified of any increases in tuition fees at re-enrolment in the programme. Further information on tuition fees and payment options is contained in our Fees Policy .

3) Student ID Cards

To produce an ID card, we need a recent photograph of you that is not obscured and is a true likeness. We will either ask you to send us/upload a photograph in advance of enrolment or take one of you at the point of enrolment. The photograph will be held on our student records system for identification purposes by administrative, academic and security/reception staff. By accepting these Terms of Admittance you are confirming that you agree to your photograph being used in this way. If you object to your photograph being used in this way please contact the University Secretary via email at gov&[email protected] . You are required to provide proof of your identity at initial enrolment and prior to the issue of your UEL student ID card. This is usually a full and valid passport but instead of this you may bring two of the following:

• A (full or provisional) driving licence showing current address
• An international driving licence
• An original birth certificate (in English)
• A debit or credit card (one only)
• A benefit book or benefit award letter (dated within the last 3 months)
• An Armed Forces Identity card
• A police warrant card

You are required to carry and display your student ID card whilst on UEL premises and must keep it safe so that it is not misused by others.

4) Proof of qualifications

You are required to produce evidence of having satisfied the entry requirements for your programme. Such evidence must be in the form of the original certificates or certified notification of results from the examining body. All qualifications must be in English or supported by an official certified translation. If you fail to provide evidence of having satisfied the requirements for the programme you are liable to be withdrawn from the programme.

5) Non-academic entry requirements

You may need to demonstrate that you have met non-academic entry requirements prior to enrolment by providing additional information to UEL. For example, if you:-

• are under 18 years of age at the time of initial enrolment,
• are applying to a programme that requires health clearance for study as stated in the programme specification,
• have declared a relevant criminal conviction,
• will be studying a programme that involves contact with children and/or vulnerable adults or leads to membership in a professional body that deals with children and/or vulnerable adults.

You will not be permitted to enrol and any offer will be withdrawn if UEL deems that you are unsuitable for study following assessment of this additional information in line with published policies. These policies will be provided to you when the additional information is requested.

6) Criminal convictions

UEL has a responsibility to safeguard staff, students and the wider community. You are required to inform UEL of any relevant criminal convictions you have and provide further information relating to these as requested. This includes any relevant criminal convictions received whilst studying at UEL. UEL will assess all information received in line with published policies and may remove you from a programme if the conviction makes you unsuitable for study in UEL's opinion. Failure to declare a relevant criminal conviction or provide further information about you may result in expulsion from UEL.

7) Providing false information to UEL

If you are discovered to have falsified or misrepresented information presented to UEL at application, enrolment or during your studies, you may be expelled from UEL.

8) Continued enrolment and student status

You are expected to abide by all UEL policies and regulations, both those in force at the time of first and subsequent enrolment and as later revised and published from time to time. UEL reserves the right to make reasonable changes to its policies and regulations and any substantial amendments will be brought to your attention. You are also required to take personal responsibility for your studies; this includes undertaking all studies in support of your programme as prescribed by UEL. Key policies include: Manual of General Regulations This describes the general regulatory framework of UEL and gives information about how UEL confers its degrees, diplomas and certificates. It includes important information about academic performance requirements for continued study. Engagement Attendance Policy This outlines UEL's expectations of students in relation to attendance on and engagement with taught programmes. These students are expected to attend all scheduled classes and engage fully with learning materials and resources provided to them - failure to do so may result in withdrawal from module(s) and/or the programme. Code of Practice for Postgraduate Research Degrees The purpose of this code is to provide a framework for the successful organisation and implementation of good practice in all matters relating to postgraduate research degrees at UEL. It aims to ensure that all students are effectively supported and supervised so that the full scope and potential of their research is realised; that their thesis is submitted within regulatory periods and that they complete their programme with a suitable and sufficient portfolio of research and employment-related skills and competencies. Health and Safety Policy This describes the structures and processes by which UEL protects the health and safety of its staff, students and visitors. It confirms that students will receive sufficient information, instruction and induction in relation to health and safety. All students should take reasonable care of their health and safety. They must abide by UEL’s rules and regulations and cooperate with supervisors to enable them to fulfil their obligations. Students must not interfere intentionally, or recklessly misuse anything provided for health and safety. UEL has consulted with its students and staff and has adopted a No Smoking Policy to safeguard the health and well-being of its community. Students are required to comply with this policy which restricts smoking to designated shelters and prohibits the use of electronic cigarettes within any UEL building or near building entrances. For further information on our Healthy Campus initiatives and support please visit the Health and Safety pages . Student Disciplinary Regulations and Procedures (incorporating the student code of conduct) This code is more than a list of things that we should and should not do: it reminds us that we should always consider how our behaviour affects others. The code applies:

• to all students;
• at all sites throughout our estate, and;
• when we represent UEL on business beyond our campus, both in real (face-to-face) and virtual environments.

And outlines expectations of students:

• verbal and physical behaviour should always be polite and respectful;
• behaviour should not impair the engagement, learning or participation of others;
• anti-social behaviour by individuals and groups will not be tolerated.

9) Changes to scheduled programmes

UEL will take all reasonable steps to ensure that the programme of study that you have accepted will conform to the programme specification published on our website and will ensure that the necessary resources required to enable you to meet the required learning outcomes and pass the relevant assessments are available. In order to ensure that our programmes are current and relevant, they are subject to regular review. From time to time, to ensure the maintenance of academic standards and/or compliance with professional body requirements, it may be necessary to amend a module or make adjustments to programme content. Major changes to programmes that in the reasonable opinion of UEL, will have a significant impact on students will involve consultation with students already enrolled on the programme when the changes are proposed. Once any changes are confirmed, UEL will notify all students and applicants of the changes. When UEL reasonably considers that the change may only impact one or more cohorts on the relevant programme, UEL may decide to only consult with the relevant cohort. In the event that we discontinue a programme, we will normally permit existing students to complete the programme within the typical duration of study. In these circumstances, UEL will use reasonable endeavours to continue the programme for existing students without making major changes. If this is not possible, we will support students in changing to another UEL programme on which a place is available, and for which the student is suitably qualified, or assist with transfer to another HEI to complete the programme elsewhere.

10) Changes to these terms

We may change these terms from time to time where, in UEL's opinion, it will assist in the proper delivery of any programme of study or in order to:- (a) Comply with any changes in relevant laws and regulatory requirements; (b) Implement legal advice, national guidance or good practice; (c) Provide for new or improved delivery of any programme of study; (d) Reflect market practice; (e) In our opinion make them clearer or more favourable to you; (f) Rectify any error or mistake; or (g) Incorporate existing arrangements or practices. No variation or amendment to these Terms of Admittance may be made without our prior written agreement. In the event that we agree to transfer you to an alternative programme of study, the transfer will be considered to be a variation to the Terms of Admittance, which shall otherwise remain in full force and existence. If we revise the Terms of Admittance, we will publish the amended Terms of Admittance by such means as we consider reasonably appropriate. We will use reasonable endeavours to give you notice of any changes before they take effect.

11) Data Protection

UEL is committed to adhering to its obligations under the Data Protection Act 2018 and will act as a Data Controller when it processes your personal data. You can find our registration to the Data controller register on ico.org.uk . UEL processes your personal data to fulfil its contractual and legal obligations to students. Personal data that we process about you includes:

• Your contact details and other information submitted during the application and enrolment processes;
• Details of courses, modules, timetables and room bookings, assessment marks and examinations related to your study;
• Financial and personal information collected for the purposes of administering fees and charges, loans, grants, scholarships and hardship funds;
• Photographs, and video recordings for the purpose of recording lectures, student assessments and examinations and for the purposes of university promotion that is in our legitimate interest but still fair to you;
• Information about your engagement with the University such as attendance data and use of electronic services such as Moodle, Civitas and YourTutor;
• Contact details for next of kin to be used in an emergency;
• Details of those with looked-after status or those who have left the care system for the provision of support;
• Information related to the prevention and detection of crime and the safety and security of staff and students, including, but not limited to, CCTV recording and data relating to breaches of University regulations;

This is not an exhaustive list, for further information please refer to our fair processing notice pages on uel.ac.uk. In all of its data processing activities, UEL is committed to ensuring that the personal data it collects stores and uses will be processed in line with the data protection principles which can be summarised as:

• Being processed lawfully, fairly and in a transparent manner;
• Collected for specified, explicit and legitimate purposes;
• Adequate, relevant and limited to what is necessary;
• Accurate and, where necessary, kept up to date;
• Kept in a form which permits identification of data subjects for no longer than is necessary;
• Processed in a manner that ensures appropriate security of the personal information;
• Be accountable for, and be able to demonstrate compliance with, the six principles above.

Student Responsibilities You must ensure that:

• All personal data provided to UEL is accurate and up-to-date. You must ensure that changes of address etc. are notified to the Student Hub.
• Students who use UEL's computing facilities may process personal data as part of their studies. If the processing of personal data takes place, students must take responsibility for that processing activity to ensure that it is in line with the data protection principles above.
• Students who are undertaking research projects using personal data must ensure that:
• The research subject is informed of the nature of the research and is given a copy of UEL's Fair Processing Notice and this Data Protection Policy.

12) Legal basis for use of data

By agreeing to these Terms of Admittance and enrolling at UEL, you are agreeing to the terms and conditions of a contract for the use of your personal data relating to your enrolment, and if appropriate, registration and ongoing participation in a programme of study. Your personal or special category data will be collected, processed, published and used by UEL, its online learning and teaching services and/or its partners and agents in ways which support the effective management of UEL and your programme of study, to allow for the delivery of bursary schemes and to support improvements to student experience and progression, and are consistent with: The terms of the Data Protection Act 2018; Any notification submitted to the Information Commissioner in accordance with this legislation; and compliance with any other relevant legislation. You have fundamental rights associated with how organisations use your personal data. Further information on data protection and use of your personal data can be found in our Data Protection Policy and on uel.ac.uk.

13) Intellectual property

You are entitled to the intellectual property rights created during your time studying at UEL that would belong to you under the applicable law. There are some programmes where the assignment of certain types of intellectual property to UEL is appropriate. UEL will require the assignment to it of intellectual property rights relating to postgraduate research that is part of an ongoing research programme. Where the nature of the research programme means that some assignment of intellectual property rights to UEL is appropriate, we will take what steps that we can to ensure that your interests are protected. UEL will take reasonable endeavours to ensure:-

• the scope of the assignment is narrow, and is restricted to what is necessary, for example, to protect UEL’s legitimate interests in the intellectual property created as party to a research programme;
• the application of the assignment is clearly defined so that it is clear to you in which circumstances the assignment will apply;
• where the assignment of the intellectual property is appropriate in the circumstances, we will take all reasonable steps to ensure that the rights of the parties are evenly balanced (for example, your work being acknowledged in a publication and, where appropriate, subject to an appropriate revenue sharing scheme)
• where UEL claims ownership of intellectual property rights in relation to a taught programme of study, such treatment of those rights will be made clear in the published information relating to that programme.

14) How we communicate with you

UEL will communicate with you via a variety of channels, including postal letters, e-mail, SMS text messages and online notices. To enable this, we request that you provide us with your e-mail address, postal address, and contact telephone number when you first enrol. Throughout your studies, it is important that you keep your contact details up to date. You can view and edit this information by logging into our student portal, UEL Direct at https://uel.ac.uk/Direct . We will create a UEL e-mail account for you after you enrol. Your e-mail address will be your student number, prefixed with a ‘u’ and followed by ‘@uel.ac.uk’ – e.g.: [email protected]. UEL will use this e-mail address to communicate with you and it is important that you regularly check and manage this mailbox for important updates and information. You can access your email account, plus information about our services, news and events by logging into our Intranet, intranet.uel.ac.uk. At the login screen, enter your email address (as above) and password. Your default UEL password will be your date of birth, formulated as DD-MMM-YY, e.g. 31-jan-84. Your UEL email account and associated UEL IT accounts will be deleted not more than 6 months after you graduate or withdraw from your programme of study (if earlier).  

15) University of East London Students' Union

The University of East London Students' Union (UELSU) represents students at UEL. By enrolling at UEL you are automatically granted membership of both UELSU and the National Union of Students (NUS). If you wish to opt out from this membership, please inform UELSU in writing at either [email protected]  or by writing to Chief Executive, UELSU, University of East London, Docklands Campus, 4-6 University Way, London E16 2RD. UELSU provides a range of services and support to students and can provide advice and representation on any matter affecting the contract between you and UEL. For further information on this support, please visit www.uelunion.org

16) Students studying at partner institutions

If you are undertaking a programme of study at a partner institution you will need to generally abide by the above terms and also those of the partner institution. Further information and support in understanding these terms is available from the Academic Partnership Office -  [email protected] .

17) International students - additional responsibilities

All international students must also comply with UK Visa and Immigration requirements. All international students are required to hold a valid visa which permits study in the UK or hold a Tier 4 visa/have applied for a Tier 4 visa with a Confirmation of Acceptance for Studies issued by UEL. Students who are being sponsored under a Tier 4 student visa must also understand and comply with the responsibilities of their student visa and cooperate with UEL in fulfilling our Tier 4 duties .

18) Equality, Diversity and Inclusion

UEL is committed to working together to build a learning community founded on equality of opportunity – a learning community which celebrates the rich diversity of our student and staff populations and one in which discriminatory behaviour is challenged and not tolerated within our community. Within the spirit of respecting difference, our equality and diversity policies promise fair treatment and equality of opportunity for all regardless of gender, ethnicity, sexual orientation, age, disability or religion/belief (or lack of). In pursuing this aim, we want our community to value and to be at ease with its own diversity and to reflect the needs of the wider community within which we operate. For further information on this inclusive approach to education please visit our Student Policies page .

19) Complaints

We welcome feedback on our programmes and services and facilitate this in a variety of ways, including programme committees, module evaluation forms and surveys. However, if you are dissatisfied with a particular service or programme or the manner in which it has been delivered, you must let the person responsible for that service know as we will always try to resolve matters at the earliest opportunity via informal conciliation. If you are unsure who to approach, please e-mail The Hub who will be able to direct your concerns appropriately. If you remain dissatisfied with a service or programme, or the manner in which it is delivered, you should refer to our formal complaints procedure to have the matter formally addressed. In addition, once you have enrolled on your programme, you will also have access to the Advice and Information Service offered by UELSU. This access is not available to students studying at partner institutions.

20) Cancellation

If you wish to cancel this contract within 14 days of enrolment in your programme, you must do so in writing. Any fees that you have paid will be refunded – please see the Fees Policy for further information on obtaining a refund.

21) Further guidance

If any of the information in these Terms of Admittance or related policies is unclear or if you have any questions, please contact The Hub for guidance on +44 (0) 208 223 4444 .

22) Right to advice

This is a consumer contract and you are able to obtain independent advice in relation to its terms and conditions from UELSU as well as your local Citizens Advice Bureau.  

23) General

Neither you nor UEL will be liable for failure to perform their obligations under these Terms of Admittance if such failure arises from unforeseeable events, circumstances or causes outside of that party's reasonable control. Examples of such events include, but are not limited to, war, terrorism, industrial disputes, natural disasters, fire and national emergencies. Only you and UEL are parties to these Terms of Admittance. No other person shall have any rights under the Contracts (Rights of Third Parties) Act 1999 to enforce any term of these Terms of Admittance. Failure or delay by you or UEL to exercise any right or remedy provided under this contract shall not constitute a waiver of that or any other right or remedy, nor shall it prevent or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall prevent or restrict the further exercise of that or any other right or remedy. These Terms of Admittance are governed by the law of England and Wales and you and UEL agree to submit to the exclusive jurisdiction of the courts of England and Wales.

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By clicking on continue , you will visit the website of École Polytechnique, one of the founding schools of Institut Polytechnique de Paris.

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• PhD Programs
• IP Paris Doctoral School

PhD in Computing, Data and Artificial Intelligence

The PhD theses conducted within the domain Computer science, data & AI of the Doctoral School of Institut Polytechnique de Paris aim at advancing the state of the art in the whole domain, starting from the most fundamental questions of computer science, related to the efficient storage and the fast processing of massive data, up to the most complex systems, like cyber-physical systems or sets of machines able to understand their environment, to interact between them and with humans, to take decisions in an autonomous way and to explain them.

The PhD works address in particular questions about:

• Computer architectures, programming languages, compilation, formal methods and proofs
• Quantum information science
• High performance computing
• Virtualization
• Cloud computing
• Data mining
• Representation of knowledge
• Operational research
• Optimization techniques
• Learning and processing techniques for images, videos, audios, texts
• Distributed systems, embedded systems, real-time systems
• Communication networks
• Internet of things,
• Robotics, autonomous systems
• Human-machine interaction
• Virtual / augmented reality
• Ddata visualisation,
• Social networks
• Cyber-security
• Control and protection of personal data.

These works have a strong technological impact thanks to the rich industrial environment of Institut Polytechnique de Paris.

• Computer Science
• Signal and Image Processing
• Automatics and Robotics
• Ecole Polytechnique
• ENSTA Paris
• Telecom Paris
• Telecom SudParis

Deep learning for multi-temporal analysis of remote sensing images, Rodrigo Daudt (Télécom Paris)

Human gesture recognition, Chuang Yu (ENSTA Paris)

PhD Studies in Data Science

Phd offering.

The BSE Data Science Center is looking for students with a strong quantitative background interested in pursuing PhD studies in areas related to data science (DS): Statistics, Machine Learning, Probability, Operations Research, and their applications in Economics. To be eligible to apply to such PhD studies students should have (or be in the final year of) a degree in Data Science, Statistics, Mathematics or related discipline. A way to acquire such a background is to first enroll in our master in Data Science Methodology ( https://bse.eu/study/masters-programs/data-science-methodology ). To follow this route, you should indicate your interest in pursuing PhD studies in your motivation letter when applying to the master. Selected students receive a tuition waiver for the master program. We emphasize however that applying to a PhD is a separate process from enrolling into the Data Science master at BSE, and in particular that doing the master is no guarantee of being admitted into the PhD afterwards. Admittance to a PhD program is done on a competitive basis, and depends on the resources available each year. There are two main routes to pursue PhD studies. 1. Apply to the PhD program of the Dept. of Economics & Business at UPF. For students doing the master in Data Science, the application would be at the end of the 1st term of the program. Students should apply to the MRes year (Year 2) of the PhD program at UPF, see  https://www.upf.edu/web/econ/phd-track  for further information on how to apply. If admitted, upon entering the program, the student takes a selection of courses and produces a Master of Research thesis, which is typically preliminary work towards the PhD thesis. Data Science students take a specially designed coursework, selected from courses in  https://www.upf.edu/es/web/econ/courses , to be agreed upon with their advisors. 2. Apply to the PhD program at a collaborating institution, such as the Statistics program at the Universitat Politècnica de Catalunya ( https://www.eio.upc.edu/en/doctorate/doctoral-program-of-the-department-of-statistics-and-operations-research ). The PhD would be under the supervision of a Data Science Center Faculty member. In such a program there is typically no coursework and students start working on their PhD thesis from day 1.

How to apply

All interested applicants should submit the materials specified below to the Data Science PhD selection committee at  [email protected] , before Jan 15. Late applications may be considered in exceptional circumstances. 1. Students interested in the PhD program at UPF must also submit an application to Year 2, following the instructions for the MRes Online Application. The deadline for that application is usually Jan 15, but double-check the UPF website.   2. Students who wish to pursue a PhD at a collaborating institution outside UPF should, in a first instance, send their application to the Data Science PhD selection committee only.   Applications to the Data Science PhD selection committee must include: – A copy of your final official undergraduate academic transcript, showing courses taken and grades obtained. – If you have finished graduate studies or are currently undergoing a master’s degree when you submit your application, a copy of the final or provisional graduate academic transcript, showing courses taken and grades obtained – A motivation letter, including a concise statement on research interests. – Two academic reference letters. If applying to the UPF program, besides uploading the letters at the UPF system they should also be emailed to  [email protected] . Please ensure that your referees send the letters by the deadline

The Barcelona School of Economics Data Science Center coordinates and promotes interdisciplinary and methodological research, training, and knowledge transfer in Data Science. The Data Science Center community consists of leading academics, machine learning researchers from industry, and practitioners from the data science and analytics industry. The research group at the Data Science Center is leading in this area and has recently been recognized by several major funding bodies, for example, the BBVA grant in Big Data, and the Google Faculty Award.

The Data Science Center is part of the Barcelona School of Economics, which is a leading institution for research and graduate education in Economics and the social sciences. The BSE offers seven Master’s programs, including a Master’s in Data Science, coordinated by the Data Science Center.

The BSE was founded as an institution for scientific cooperation between four existing academic and research units with a long tradition of collaboration: Institut d’Anàlisi Econòmica, Centre de Recerca en Economia Internacional, Universitat Autònoma de Barcelona, and Universitat Pompeu Fabra. It continues to focus on consolidating strong research groups across these four centers, of which the Data Science Center is an example.

Universitat Pompeu Fabra (UPF) is a public, international and research-intensive university that, in just twenty-five years, has earned a place for itself among the best universities in Europe. Awarded with a CEI label (International Excellence Campus) by the Spanish Ministry of Education, the University also figures in some of the most influential rankings UPF has recently been featured as the 5th fastest-rising young university in the world by Times Higher Education, while the Department of Economics at the university is consistently ranked in the top 40 QS World University Rankings by Subject.

Contact Data Science Center Barcelona School of Economics Ramon Trías Fargas, 25-27 08005 Barcelona, Spain.

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(in collaboration with scuola normale superiore, sant’anna school, university of pisa and national research council).

The Program

The PhD in Data Science is aimed at educating the new generation of researchers that combine their disciplinary competences with those of a “data scientist”, able to exploit data and models for advancing knowledge in their own disciplines, or across diverse disciplines. To this purpose, the PhD in Data Science develops a mix of knowledge and skills on the methods and technologies for the management of large, heterogeneous and complex data, for data sensing ( how to harvest data ), for data analysis and mining ( how to make sense of data ), for data visualization and storytelling ( how to narrate data ), for understanding the ethical issues and the social impact of Data Science. The PhD students will have the opportunity of developing data science projects in a variety of domains, including:

• Data science for society and policy
• Data science for economics and finance
• Data science for culture and the humanities
• Data science for industry and manufacturing
• Data science for biology and health
• Data science for the hard and environmental sciences
• Data science ethics and legal aspects
• Data science techniques and methods

The PhD leverages the critical mass of data science labs and researchers accumulated in Pisa since early 2000’s, across the University of Pisa, the ISTI and IIT institutes of the CNR (National Research Council), Scuola Normale Superiore, Sant’Anna School of Advanced Studies and the IMT School for Advanced Studies Lucca. These labs gave rise to pioneering European projects in big data analytics and data science, as well as to the earliest educational programs for data scientists at graduate and PhD level. In 2015, the European Commission has chosen this hub as the coordinator of the European Research Infrastructure for Big Data Analytics & Social Mining,  SoBigData     http://www.sobigdata.eu .  This initiative provides an ecosystem of data, analytics and competences to support inter-disciplinary open data science and data-driven innovation, within an ethical framework of transparency, privacy, and responsibility. SoBigData provides a unique platform for doctoral education in Data Science, recognized by the Ministry of Education, University and Research   [1] , where PhD students can carry out multi-disciplinary data-driven research.

The IMT School Representative for the Program is Prof. Rocco De Nicola .

   [1]  Rapporto MIUR BigData,    http://www.istruzione.it/allegati/2016/bigdata.pdf   pag. 33

For further information, please visit the   Program's website .

Teaching Activity

Teaching is articulated in two lines: alignment of data science skills, to create a common ground for students with diverse background, and applications of data science in disciplinary and multi-disciplinary contexts. For alignment, PhD students will have the opportunity to take selected courses offered by the post-graduate Master in “Big Data Analytics and Social Mining” (Master Big Data) of the University of Pisa, in collaboration with CNR, Scuola Normale Superiore, Sant’Anna School of Advanced Studies and SoBigData.eu. Available courses cover the basics of Data Science and Big Data Analytics:

• Big Data Sensing & Procurement (Analytical Web Crawling, Scraping, Web Search and Information Retrieval, Semantic Text Annotation, Big Data Sources, Crowdsensing)
• Big Data Mining (Data Mining, Machine Learning and Statistical Learning, Network Science and Social Network Analysis, Mobility Data Analysis, Web Mining, Nowcasting, Sentiment Analysis and Opinion Mining)
• Big Data Storytelling (Visualization, Visual analytics, Data Journalism)
• Big Data Ethics (Privacy-by-design, Data Protection Regulations, Responsible Data Science, Legal aspects of Data Science)
• Big Data Technologies (Data Management for Business Intelligence, High Performance & Scalable Analytics, NO-SQL Big Data Platforms).

A wide variety of PhD courses focusing on the multi-disciplinary applications of data science are offered by the participating institutions, also in synergy with existing disciplinary PhD programs. Students also have the opportunity to participate in summer schools organized in collaboration with international research institutions, and to the PhD+ program of the University of Pisa, for the development of entrepreneurial and innovation skills.

The PhD Board

Dino Pedreschi   (PhD Program Coordinator), University of Pisa Albert-Laszlo Barabasi , Northeastern University, Boston, USA Vincenzo Barone , Scuola Normale Superiore Roberta Bracciale , University of Pisa Chiara Cappelli , Scuola Normale Superiore Alessandro Cellerino , Scuola Normale Superiore Francesca Chiaromonte , Sant’Anna school of Advanced Studies Giulio Cimini , IMT School for Advanced Studies Lucca Marco Conti , National Research Council (CNR) Tommaso Cucinotta , Sant’Anna school of Advanced Studies Giuseppe De Pietro , National Research Council (CNR) Fabio Gadducci , University of Pisa Diego Garlaschelli , IMT School for Advanced Studies Fosca Giannotti , National Research Council (CNR) János Kertész , Central European University, Budapest Fabrizio Lillo , Università di Bologna Pietro Luigi Lopalco , University of Pisa Francesco Marcelloni , University of Pisa Stan Matwin , Dalhousie University, Halifax, CDN Anna Monreale , University of Pisa Elena Pavan , Scuola Normale Superiore Alex “Sandy” Pentland , MIT, USA Raffaele Perego , National Research Council (CNR) Andrea Piccaluga , Sant’Anna school of Advanced Studies Nadia Pisanti , University of Pisa Monica Pratesi , University of Pisa Chiara Maria Angela Roda , University of Pisa Salvatore Ruggieri , University of Pisa Tiziano Squartini , IMT School for Advanced Studies Lucca Franco Turini , University of Pisa

Call for applications

Details on upcoming and past calls for applications are available on the Program's website .

We have 924 data science PhD Projects, Programmes & Scholarships

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data science PhD Projects, Programmes & Scholarships

Gendered perspectives of data science, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

PhD Positions (f/m/x) in Data Science

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Germany PhD Programme

A German PhD usually takes 3-4 years. Traditional programmes focus on independent research, but more structured PhDs involve additional training units (worth 180-240 ECTS credits) as well as placement opportunities. Both options require you to produce a thesis and present it for examination. Many programmes are delivered in English.

EPSRC Centre for Doctoral Training in Healthcare Data Science (HDS CDT)

Funded phd programme (uk students only).

Some or all of the PhD opportunities in this programme have funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

4 Year PhD Programme

4 Year PhD Programmes are extended PhD opportunities that involve more training and preparation. You will usually complete taught courses in your first year (sometimes equivalent to a Masters in your subject) before choosing and proposing your research project. You will then research and submit your thesis in the normal way.

Fully funded PhD positions in Astronomy, Biology, Computer Science, Chemistry & Materials, Data Science & Scientific Computing, Earth Science, Mathematics, Neuroscience, and Physics

International phd programme.

International PhD programs are often designed for international students. Your PhD will usually be delivered in English, though some opportunities to gain and use additional language skills might also be available. Students may propose their own PhD topics or apply for advertised projects.

Data Science and AI for Real-world Optimisation Problems

Competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Data Science Research in Health and Well-Being

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Numerical Algorithms for Molecular Systems and Data Science

Developing and applying semantic representations of scientific knowledge, advanced analytics to investigate novel bp wearable technology to improve cardiovascular outcomes and falls, funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

1 Year - MRes Project - Playable Archives: Creating engagement with specialist collections through data-driven tools and digital innovation

Industry hopping and business transformation for non-data driven businesses, development and validation of the epilepsy-heart syndrome: a focus on ictal asystole and developing evidence-based national consensus guidelines, phd scholarship in data science and robot planning, modelling the impact of diagnostic pathways in cancer and cardiovascular disease - university of swansea (part of health data research uk’s big data for complex disease driver programme), harnessing human genomic, transcriptomic and proteomic data to identify novel therapeutic targets.

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Professional Doctorate Data Science

The first industrial doctorate of its kind will equip you with interdisciplinary research and practical skills for a job in data science or data analytics.

• Award ProfD
• Start date January 2025, September 2025, January 2026
• Application deadline $value
• Duration Doctorate full-time: 36 months, Doctorate part-time: 48-72 months
• Mode of study full time, part time
• Delivery on campus

Our Professional Doctorate in Data Science is the first industrial doctorate of its kind, and is supported by The Data Lab innovation centre.

We build on Stirling’s highly successful taught MSc Data Science to equip you with a range of cutting-edge, interdisciplinary research and practical skills and tools, that will lead to an academic or industry job in the area of Data Science, with possible applications to sectors such as life-sciences, finance, engineering, computing, healthcare, fintech or business.

In addition to enhancing students’ employability through work-based learning, the doctorate prepares you to undertake interdisciplinary Data Science research, jointly supervised by world-leading Stirling academics and Data Science industry experts.

The Professional Doctorate consists of a one-year taught programme, based on Stirling MSc programmes in Data Science, and a two-year research programme, to be conducted in collaboration with an industrial partner around industry-relevant research questions. Students could be employees of the industrial partner looking for further training and qualification, or have already established a (potential) collaboration with an industrial partner willing to support the project.

Each of our MSc in Data Science or in Fintech may offer the opportunity to establish a suitable collaboration with an industrial partner, and then grant access to the second year of the Professional Doctorate in Data Science on a research programme agreed with the industrial partner.

Specific projects and collaborations can be considered on a case-by-case basis. An (in principle) agreement with an identified partner company is necessary for the research component of the program.

Top reasons to study with us

Course objectives.

This professional/industrial doctorate is designed to:

• Equip professionals with the required multi-disciplinary skills, and underlying theoretical, practical and transferable knowledge, to undertake practitioner-oriented, impact-led research in data science.
• Give sound training in relevant practical, investigative, analytical and generic skills required for research in the area of data science.
• Experience of data science challenges and applications in a wide range of areas, such as business, healthcare, life science, fintech and scientific disciplines.
• Provide the opportunity to plan, undertake and prepare publication quality research.

Work placements

The research component of the Professional Doctorate in Data Science is a project of industrial interest to be carried out in collaboration with a company supporting the project.

Flexible learning

If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.

Faculty facilities

The Professional Doctorate can be attended both as a full time or part-time course. The taught component is organised around learning material provided online, contact teaching and tutorial hours, and an “open-door” approach allowing students a direct contact with lecturers, providing for great flexibility in the organisation of study. The research component consists of a research project whose development can be planned by agreement between the student, the company and the academic supervisor.

If you’re interested in studying a module from this course, the Postgraduate Certificate or the Postgraduate Diploma then please email Graduate Admissions to discuss your course of study.

Entry requirements

Academic requirements.

Students applying may have a variety of backgrounds including:

• numerate and computational degrees (computing, mathematics, physics, engineering)
• medical/clinical, business, marketing or economics background, plus some relevant work (industrial or commercial) experience

Students may also come from other science or engineering backgrounds, to gain applied research and analytical skills that are in high demand in the Scottish job market.

Students with suitable research-oriented Masters degrees in numerate and computational disciplines (computing, mathematics, physics, engineering), will be considered for direct entry to the second year of the Doctoral Training Component, on a case-by-case basis.

An established, in-principle or under-discussion agreement with an industrial partner interested in collaborating and supporting the research component of the programme should be in place.

International entry requirements

View the entry requirements for your country.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:

• IELTS Academic or UKVI 6.0 with a minimum of 5.5 in each sub-skill.
• Pearson Test of English (Academic) 60 overall with a minimum of 59 in each sub-skill.
• IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing.

See our information on English language requirements for more details on the language tests we accept and options to waive these requirements.

Pre-sessional English language courses

If you need to improve your English language skills before you enter this course, our partner INTO University of Stirling offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for entry to this degree.

Find out more about our pre-sessional English language courses .

Course details

You will undertake a number of taught modules to equip you with the skills required for data science research. These modules are taught through lectures, practicals and small group work and are assessed through a variety of course work and exams.

Compulsory modules:

• Mathematical Foundations (10 credits)
• Statistics for Data Science (10 credits)
• Representing and Manipulating Data (20 credits)
• Commercial and Scientific applications (20 credits)
• Relational and non-relational databases (20 credits)
• Data Analytics (20 credits)
• Cluster Computing (20 credits)
• Research Dissertation project (60 credits)

To prepare for the professional doctorate, an independent research project (60 credits) will include a systematic review of an appropriately challenging applied research topic/area, and development of a full Doctorate research proposal as outputs – assessed through an oral viva exam and research poster presentation.

Following the taught component, you will undertake a period of industry-led applied research (360 level 12 credits) by working with experienced academic and industrial supervisors, on original piece(s) of an applied research project. The project could either be a single long project or a portfolio of data-centric projects, depending on the industrial organization’s strategic priority needs. Outcomes will be presented in a doctoral dissertation assessment through a viva examination by internal and external examiners.

Course Details

The taught component of the Professional Doctorate spans across the first year and mutates the modules from the various MSc in Data Science, and includes an advanced dissertation project with an assessment of the state of the art and research plan for the next two years.

The research component consists of a period of industry-led applied research, carried out by working with experienced academic and industrial supervisors, on original piece(s) of an applied research project. The project could either be a single long project or a portfolio of data-centric projects, depending on the industrial organisation’s strategic priority needs. Outcomes will be presented in a doctoral dissertation.  

Assessment of the taught component of the program follows the standard assessment of MSc modules and may consists of a variety of assessment strategies, including written assignments, exams,  individual projects, collaborative and group work, lab work, presentations and reports and a dissertation project.

The doctoral dissertation will be assessed through a viva examination by an internal and an external examiner (as in a PhD viva).

Assessment will be tailored to students’ special needs, where appropriate.

Course director

Dr Andrea Bracciali

[email protected] +44 (0)1786 467446

Fees and funding

Fees and costs.

This fee is charged as an annual course fee. If you need to extend your period of study or repeat study, you will be liable for additional fees. Your fees will be held at the same level throughout your course.

For more information on courses invoiced on an annual fee basis, please read our tuition fee policy .

Doctoral loans

If you're domiciled in England or Wales you may be eligible to apply for a doctoral loan from your regional body:

• English students can apply for a loan of up to £28,673 from  Student Finance England .
• Welsh students can apply for a loan of up to £28,395 from  Student Finance Wales .

Funding 

Eligible international students could receive a scholarship worth between £4,000-£7,000.  See our range of generous scholarships for international postgraduate students .

University of Stirling alumni will automatically be awarded a fee waiver for the first year of Masters studies through our Stirling Alumni Scholarship .

Applicants from the UK or Republic of Ireland who hold a first-class honours degree or equivalent will automatically be awarded a £2,000 scholarship through our  Postgraduate Merit Scholarship .

If you have the talent, ability and drive to study with us, we want to make sure you make the most of the opportunity – regardless of your financial circumstances.

Learn more about available funding opportunities or use our scholarship finder to explore our range of scholarships.

Additional costs

There are some instances where additional fees may apply. Depending on your chosen course, you may need to pay additional costs, for example for field trips. Learn more about additional fees .

Cost of living

If you’re domiciled in the UK, you can typically apply to your relevant funding body for help with living costs. This usually takes the form of student loans, grants or bursaries, and the amount awarded depends upon your personal circumstances and household income.

International (including EU) students won’t normally be able to claim living support through SAAS or other UK public funding bodies. You should contact the relevant authority in your country to find out if you’re eligible to receive support.

Find out about the cost of living for students at Stirling

Payment options

We aim to be as flexible as possible, and offer a wide range of payment methods - including the option to pay fees by instalments. Learn more about how to pay

After you graduate

Demand for people with data science skills is projected to grow rapidly in the coming years attracting high salaries.

Our Professional Doctorate in Data Science is run in partnership with industry and is designed to produce graduates with the skills that companies need.

Employability skills

The Doctorate programme, equivalent to an Engineering Doctorate (EngD), is aimed at a clear and distinct market of professionals seeking to enhance their employability opportunities through applied, impact-led research. You’ll learn to develop and validate innovative, data-driven and evidence-based approaches within your chosen career. The programme is geared towards enhancing both your applied, multi-disciplinary research and employability skills in data science.

The doctorate is open to any profession where data-driven and data-intensive research, and its informational derivatives, are central to the development of sustainable business and industry models, including decision-making, project and risk evaluation, policy and technology development. The doctorate research component is relevant to the student’s professional setting and career aspirations.

Companies we work with

Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The Data Lab collaborates with the University of Stirling to help deliver the course, and provide funding and resources for students. You can find out more about the Data Lab from their web site .

We have also developed this professional doctorate in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Data Science projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The first year of the course features a long Industry-led research dissertation project, generally in partnership with a company or technology provider. This provides students with a showcase of their skills to take to employers or launch online.

We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Related courses

• MSc Artificial Intelligence
• MSc Big Data
• MSc Big Data (Online)
• MSc Business Analytics
• MSc Data Science for Business
• MSc Finance and Data Analytics
• MSc Financial Technology (FinTech)
• MSc Marketing Analytics
• MSc Mathematics and Data Science
• MSc Social Statistics and Social Research

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LSE PhD Studentship in Data Science

For 2023 entry, LSE is offering a doctoral studentship for PhD study affiliated to the Data Science Institute (DSI). 

Applications are welcome from both students applying to core data science programmes (Statistics, Mathematics, or Methodology) as well as from applied departments across the School, as long as their projects involve data science or computational social science methods.

The successful student will join a growing cohort of existing DSI-hosted PhD students as well as a regular stream of visiting PhD students in data science. 

Eligibility

Selection for this studentship is on the basis of outstanding academic merit and research potential. This relates both to your past academic record and to an assessment of your likely aptitude to complete a PhD in your chosen topic in the time allocated.

Scholarship amount

The LSE Data Science PhD Studentship is tenable for four years and covers full fees along with an annual stipend of £19,668 (2022/23 rate).

How to apply

To be considered, you must submit a complete application (including references, proposal, marked work etc) by the funding deadline below.  

• funding deadline for all LSE PhD Studentships for 2023 entry: 13 January 2023

For more information visit  how to apply  for a place on a PhD programme.

Fees and funding Scholarships, studentships, loans and tuition fees

How to apply The application process, UCAS and when to apply

Undergraduate fees and funding Details on available scholarships, bursaries, loans and tuition fees

Graduate fees and funding Details on available scholarships, bursaries, loans and tuition fees

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PhD Studies & Research

Science and research in Germany are characterised by a distinguished infrastructure, a wide variety of disciplines, well-equipped research facilities and competent staff. Germany offers various career opportunities for international PhD students and researchers.

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Data Science, MS

Cis graduate program coordinator, related resources.

• Computer and Information Science
• College of Engineering and Computer Science

Profit From the Growing Demand for Data Science Professionals

The demand for sophisticated data analysis is exploding in business, industries, government, health care, nonprofit organizations, and elsewhere. Our Master of Science in Data Science at the University of Michigan-Dearborn provides the advanced knowledge and skills you need to excel in this exciting and challenging field.

You can complete the program fully online, in person, or a combination of both. We schedule all courses during late afternoons and evenings, making them convenient for part-time students who work.

Where a MS in Data Science Degree Will Take You

Our interdisciplinary curriculum, which integrates areas like mathematics, statistics, computer science, and information science, provides you with a diverse and comprehensive foundation on which to build your career. With data driving decisions in so many fields, the opportunities for data analysts, business intelligence analysts, data architects, data scientists, and related professionals are vast and growing.

Career and Salary Outlook

The information in this table is meant to give you an idea of career opportunities with this degree. All information is pulled from the Bureau of Labor Statistics and is meant to be averages across the United States in 2022. Please note that these figures reflect overall averages and may not represent entry-level salaries.

Mathematicians and Statisticians

$104,860 Median Salary

Overall employment of mathematicians and statisticians is projected to grow 30 percent from 2022 to 2032, much faster than the average for all occupations. About 3,500 openings for mathematicians and statisticians are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Data Scientists

$108,020 Median Salary

Employment of data scientists is projected to grow 35 percent from 2022 to 2032, much faster than the average for all occupations. About 17,700 openings for data scientists are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Database Administrators and Architects

$117,450 Median Salary

Overall employment of database administrators and architects is projected to grow 8 percent from 2022 to 2032, faster than the average for all occupations. About 10,200 openings for database administrators and architects are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Operations Research Analysts

$83,640 Median Salary

Employment of operations research analysts is projected to grow 23 percent from 2022 to 2032, much faster than the average for all occupations. About 9,800 openings for operations research analysts are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Computer and Information Research Scientists

$145,080 Median Salary

Employment of computer and information research scientists is projected to grow 23 percent from 2022 to 2032, much faster than the average for all occupations. About 3,400 openings for computer and information research scientists are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.

Program Details

What you’ll study.

You have tremendous flexibility to tailor the 30-credit program to your personal interests and professional needs. The program includes 18 hours of core courses, but only one is required. For the remainder, you choose one course from each of five groups.

Practical Skills You’ll Gain

Our MS in Data Science program emphasizes learning by doing. You'll have the opportunity to work on real-world projects and be exposed to complex data analytics problems. Our Data Science/Management Research Laboratory gives you an opportunity to collaborate with our expert faculty on research funded by esteemed sponsors like the National Science Foundation, the National Institutes of Health, IBM, and Ford Motor Company.

Our  internship ,  research , and  study abroad programs offer a wealth of hands-on experiences for data science students. Check out the pages for these programs, and talk to your professors to learn more.

Admission Requirements

• Bachelor degree in a Science, Technology, Engineering, or Mathematics (STEM) field earned from an accredited program with an average of B or better. 
• Transcripts of prior college work. 
• Two letters of recommendation. At least one letter must be from someone familiar with the candidate's academic performance. 
• Completed one course in probability and statistics, one course in programming, and one course in calculus II. 

Programming

• CIS 200/CIS 2001 (or equivalent) required

Mathematics

• MATH 116 (or equivalent) required
• MATH 215 (or equivalent) recommended
• MATH 227 (or equivalent) recommended

Statistics (one of these or equivalent required)

• A course in calculus III and a course in linear algebra are recommended but not required. 

Ready to Apply?

Estimate your cost of attendance per semester.

Application Deadline

This program operates on a rolling admission basis. It is advised to submit all documents by the advisory deadline to ensure consideration for your preferred term and to facilitate pre-term planning. Advisory deadlines differ for domestic and international students.

Graduate Scholarships

Learn about scholarship opportunities for new and continuing graduate students.

Programs Offered Beyond MS

Computer and information science, ms, related programs, artificial intelligence, ms, software engineering, ms.

Become a part of the UM-Dearborn community and put your dreams to work for you.

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Explore Exciting Fully Funded Doctoral (PhD) Positions at Leading Universities in Europe and the UK!

Are you eager to begin an exceptional academic journey in Europe and the UK?

We are thrilled to announce the availability of multiple fully-funded PhD positions at leading universities and institutes across Europe and the UK. These exceptional opportunities are open to international students, allowing talented individuals from diverse backgrounds to further their academic careers.

The PhD positions are offered in the following esteemed institutes:

🇦🇹 AIT Austrian Institute of Technology GmbH 🇦🇹 Technische Universität Wien 🇦🇹 Complexity Science Hub Vienna 🇩🇪 University Clinic Bonn 🇩🇪 Fraunhofer HHI 🇩🇪 Ruhr University Bochum 🇩🇪 Saarland University 🇩🇪 Ludwig-Maximilians-Universität 🇧🇪 GIGA Institute-ULiège 🇧🇪 Université catholique de Louvain 🇮🇹 Consorzio Nazionale Interuniversitario per le Telecomunicazioni 🇪🇸 Bcam - Basque Center For Applied Mathematics 🇪🇸 URV ECoMMFiT 🇪🇸 Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC) 🇳🇱 Wetsus - European Centre of Excellence for Sustainable Water Technology 🇳🇱 Eindhoven University of Technology (TUe) 🇩🇰 Aarhus University 🇫🇷 IFP Energies nouvelles (IFPEN) 🇫🇷 Ecole Centrale de Nantes 🇫🇷 Université de Strasbourg 🇳🇴 University of Bergen 🇫🇮 Aalto University 🇬🇧 The University of Edinburgh 🇨🇿 Biology Centre CAS

PhD Student in Transport Modelling for Sustainable Mobility, AIT Austrian Institute of Technology GmbH (Austria)

PhD Student in Immunology, University Clinic Bonn (Germany) 

PhD Student In Harmonic Analysis, Bcam - Basque Center For Applied Mathematics (Spain)

PhD student working on in silico and in vitro models of osteoarthritis, GIGA Institute-ULiège (Belgium)

PhD Student in Architecture design and effective routing for optical multi-layered space/aerial networks, Fraunhofer HHI (Germany)

PhD Student in Continuous gas fermentation with of Clostridium carboxidivorans, 

Technische Universität Wien (Austria)

PhD Student in Creation and application of charged nano bubbles, Wetsus - European centre of excellence for sustainable water technology (Netherlands)

PhD Student in Complexity Science, Complexity Science Hub Vienna (Austria)

PhD Student in Parameterization of the SAFT thermodynamic model using machine learning, IFP Energies nouvelles (IFPEN) (France)

PhD Student in Healthy Oats: Optimising Parameters for Oat Growth, Bioactive Extraction, and Processing to Produce Sustainable Fortified Food-For-Health Products, Atlantic Technological University (Ireland)

PhD Student in simulation/experiments of multiphase flows, URV ECoMMFiT (Spain)

PhD Student in simulations of nanoscale water confinement and transport, Ruhr University Bochum (Germany)

PhD Student in Single cell mechanobiology and soft matter metrology, Aarhus University (Denmark)

PhD Student in Novel bipolar membranes by electrospinning, Wetsus - European centre of excellence for sustainable water technology (Netherlands)

PhD Student in Computational Fluid Mechanics, Ecole Centrale de Nantes (France)

PhD Student in Modeling and numerical simulation of swelling in thermoplastic polymer parts, Université catholique de Louvain (Belgium)

PhD Student in Automotive radar-centric ISAC system design (CNIT-3), Consorzio Nazionale Interuniversitario per le Telecomunicazioni (Italy)

PhD Student in AI to Combine and Model Electromagnetic Noise Footprint (EMNF) in PCB Tracks, Eindhoven University of Technology (TUe) (Netherlands)

PhD Student in physical oceanography, University of Bergen (Norway)

PhD Student in PFAS-remediation in water treatment by Foam Fractionation, Wetsus - European centre of excellence for sustainable water technology (Netherlands)

PhD Student in Network slicing and AI-based resource allocation for optical multi-layered non-terrestrial networks, Aalto University (Finland)

PhD Student in Molecular dynamics simulations of X-ray scattering experiments, Saarland University (Germany)

PhD Student in Simulation of Sustainable Permanent Magnets based on High Entropy Alloys, Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC) (Spain)

PhD Student in Automated Machine Learning, Ludwig-Maximilians-Universität (Germany)

PhD Student in Theoretical Quantum Many-Body Physics, Université de Strasbourg (France)

PhD Student in QKD satellite network optimisation based on realistic atmospheric channel models, The University of Edinburgh (United Kingdom)

PhD Student in Plant Biophysics and Biochemistry, Biology Centre CAS (Czech Republic)

PhD Fellow in Environmental Engineering, Agronomy, Biology, and related fields, 

Instituto de Engenharia Mecânica (Portugal)

Don't miss the opportunity, apply now!

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