phd in data science in sweden

PhD programmes

When you apply for a phd programme in sweden, you’re actually applying for a job. yeah, you read that right. 👀.

“Do a master’s here – I’d say it’s a very good step to doing a PhD in Sweden. I did my master’s at Malmö University and now I’m doing a PhD at Lund University” – Sanjay, Malmö University. Photo: Oskar Omne

So that means – no tuition fees, no scholarships. But you’ll receive a monthly salary instead. Nice, eh? And that’s why available PhD positions are listed on a university’s job board.

How to apply for a PhD position? You’ll apply directly to the university.

Just so you know, there’s no centralised application process. And things like requirements and application dates? This kind of stuff is decided by each department. But we do know that you’ll need to have a master’s degree – in the same field of study – and a great level of English to apply for a PhD here. You might even need to be fluent in Swedish. But that’ll depend on the subject.

+ - Find a PhD at a Swedish university

• PhD at Blekinge Institute of Technology ↗️
• PhD at Chalmers University of Technology ↗️
• PhD at Dalarna University ↗️
• PhD at Halmstad University ↗️
• PhD at Jönköping University ↗️
• PhD at KTH Royal Institute of Technology ↗️
• PhD at Karlstad University ↗️
• PhD at Karolinska Institutet ↗️
• PhD at Konstfack ↗️
• PhD at Linköping University ↗️
• PhD at Linnaeus University ↗️
• PhD at Luleå University of Technology ↗️
• PhD at Lund University ↗️
• PhD at Malmö University ↗️
• PhD at Marie Cederschiöld University ↗️
• PhD at Mälardalen University ↗️
• PhD at SLU (Swedish University of Agricultural Sciences) ↗️
• PhD at Stockholm School of Economics ↗️
• PhD at Stockholm University ↗️
• PhD at Stockholm University of the Arts ↗️
• PhD at the Swedish Defence University ↗️
• PhD at Södertörn University ↗️
• PhD at Umeå University ↗️
• PhD at University College Stockholm ↗️
• PhD at University West ↗️
• PhD at University of Borås ↗️
• PhD at University of Gothenburg ↗️
• PhD at University of Gävle ↗️
• PhD at University of Skövde ↗️
• PhD at Uppsala University ↗️
• PhD at Örebro University ↗️

+ - Find other academic positions in Sweden

• University Positions ↗️
• Academic Positions ↗️
• EURAXESS academic positions in Europe ↗️

+ - Useful info about research

• CORDIS ↗️ – EU Research and Information Service
• EURAXESS information ↗️ – Portal for information and practical assistance for researchers moving to Sweden
• Formas ↗️ – The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning
• Swedish Council for Health, Working Life and Welfare ↗️
• Swedish Research Council ↗️
• Vinnova — Sweden’s Innovation Agency ↗️ – An organisation that integrates research and development in technology, transport and working life
• Handbook for International Researchers ↗️ – Stockholm University’s handbook for international researchers

• 👩🏽‍🎓 Beyond the master's: a PhD?
• Find a research position at a Swedish university ↗️

23 Best universities for Data Science in Sweden

Updated: February 29, 2024

• Art & Design
• Computer Science
• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
• Mathematics

Below is a list of best universities in Sweden ranked based on their research performance in Data Science. A graph of 334K citations received by 11.2K academic papers made by 23 universities in Sweden 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. Uppsala University

For Data Science

2. KTH Royal Institute of Technology

3. Lund University

4. Karolinska Institute

5. Linkoping University

6. Chalmers University of Technology

7. Stockholm University

8. University of Gothenburg

9. Umea University

10. Blekinge Institute of Technology

11. Lulea University of Technology

12. University of Skovde

13. Linnaeus University

14. Orebro University

15. Swedish University of Agricultural Sciences

16. Malmo University

17. Malardalen University

18. Boras University College

19. Stockholm School of Economics

20. Karlstad University

21. Mid Sweden University

22. Jonkoping University

23. Halmstad University

The best cities to study Data Science in Sweden based on the number of universities and their ranks are Uppsala , Stockholm , Lund , and Linkoping .

Computer Science subfields in Sweden

1,138 data-science-phd positions in Sweden

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PhD student in Computer and Systems Sciences , with focus on Data Science

Ref. No. SU FV-0880-24 at the Department of Computer - and Systems Sciences . Closing date: (15 April 2024). Prolonged application time - new closing date: 29 April 2024. The Department of Computer

Doctoral ( PhD ) student in data -driven imaging, bioinformatics, and experimental biology for personalized atherosclerotic risk prediction

patients, together representing the world’s largest resource for molecular characterization of human atherosclerosis. The PhD student project will use data available in the BiKE resource to explore

PhD Student in Machine Learning for Information /Communication Technology

accuracy. At the Department of Electrical Engineering , we conduct internationally renowned research spanning many fields and including information and communication theory, machine learning, and signal

PhD positions in mathematical statistics at Stockholm University

10 Apr 2024 Job Information Organisation/Company Stockholm University Research Field Computer science Researcher Profile First Stage Researcher (R1) Country Sweden Application Deadline 23 Apr 2024

PhD student position in neutron scattering studies of 2D hybrid organic-inorganic perovskites for energy applications.

We are looking for a highly motivated PhD student, which a background in physics, chemistry or materials science , who will develop new fundamental understanding of, so called, phosphors, for use in

PhD in Biochemistry

17 Apr 2024 Job Information Organisation/Company Uppsala universitet Department Uppsala University, Department of Chemistry - BMC Research Field Biological sciences Chemistry Researcher Profile

PhD -student in Economics

information about the subject, read more here: Economics . Duties As a PhD student, you are expected to be part of the research environment at the Division. This means gaining practical research experience by

PhD position in Biochemistry

Uppsala University, Department of Chemistry – BMC PhD student in Biochemistry Structure and dynamics of photoreceptor proteins using single-particle cryo EM. Department of Chemistry – BMC conducts

Doctoral ( PhD ) student position in Molecular Epidemiology with application in Precision Health

information can be found at http://ki.se/en/meb This PhD program is part of the SciLifeLab and Wallenberg National Program for Data -Driven Life Science (DDLS). Data driven life science Data -driven life science

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Doctoral studies in Computing Science and Computational Science

We offer third-cycle programmes in a broad range of specializations in the field of Computing Science. The programmes are intended to instruct the student in research on a high international level, in collaboration with other researchers in Sweden and abroad, and to present the research and findings in a convincing manner. The group of doctoral students usually consists of up to 30 doctoral students from a large number of different countries.

Director of PhD studies

Database systems, Distributed Systems, Cognitive Computing, Intelligent Robots, Artificial Intelligence and Human-Computer Interaction, Theoretical Computer Science, Language Processing, Optimization, Matrix Computations and High-performance Parallel Computing are the most important areas in which we are educating future researchers. Each doctoral student belongs to one of the research groups at The Department of Computing Science , consisting of both doctoral students and senior researchers.

The third-cycle programmes are largely characterized by individuality and personal contact between the supervisors and the doctoral student. The programmes include a few courses which provide the basic skills required to become a successful researcher. Other courses, which are often individually adapted, focus on acquiring in-depth knowledge in the subject area of the thesis. All doctoral students in the area of Computing Science also take the course "Doctoral Training Days in Computing Science". Here, the doctoral students present their research projects and findings to one another and to the entire Department of Computing Science, twice per year.

However, doctoral studies consist first and foremost in conducting research together with a supervisor and others, which results in scientific articles and eventually a doctoral thesis. Most doctoral students are involved in externally financed international research projects. Doctoral students generally tend to present their scientific work at international conferences.

Read more about our programmes and the city of Umeå.

Read more about doctoral studies (PhD) at the Department of Computing Science

Researchers have found a way to better user experiences through something called self-driving microservices.

PhD Timotheus Kampik shows mathematical reasoning methods for autonomous intelligent systems.

Chanh Nguyen has developed methods to improve the efficiency of so-called Mobile Edge Clouds..

General syllabus

Computing Science

Computational Science and Engineering

PhD programme in statistics

A PhD in statistics yield a very strong foundation in the future labour market, where there is more demand being placed on competency in data analysis. The PhD programme in statistics at Uppsala University provide both a broadening and deepening in statistics, providing skills in modern statistics methods and research areas. Within in the PhD programme in statistics, one should also apply statistical methods on practical applications, thereby increasing the proficiency with which to analyse data. We mainly supervise our PhD students within our four main research areas: High dimensional data ,  causal inference ,  structural equation modeling  and  time series econometrics .

Qualifications

Basic and specific qualifications to be admitted to the PhD programme is described in the  general study plan . Aside from basic qualifications for a PhD programme, the applicant must have passed results on courses of 90 credits in statistics, of which at least 60 credits at advanced level.

Arrangement of the PhD programme

The PhD programme consists of four years of full-time studies, and consists of 90 credits of coursework and a thesis worth 150 credits. Of the 90 coursework credits, 34.5 consists of mandatory courses, with the rest being elective courses.

The mandatory courses are: Inference theory (15 credits), Asymptotic theory (7.5 credits), Philosophy of science (5 credits), Scientific communication (5 credits) and Research ethics (2 credits), or equivalent courses. For PhD students who does not have prerequisite knowledge in probability theory, the course Probability theory (7.5 credits) is also mandatory.

The remaining courses are elective courses but should be within one or more of the areas of statistical methodology and/or applications. Courses both within and outside for the thesis area should be included, with at least 15 credits for each.

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The industry graduate school Data Intensive Applications (DIA)

Data Intensive Applications (DIA) is a graduate school for industrial doctoral students that focuses on applied research, addressing the big data and artificial intelligence challenges of our industry partners. The industry graduate school is funded by the Knowledge foundation, Linnaeus University and the participating companies.

Wanted: Industrial doctoral students with a focus on data-intensive applications

The industry graduate school Data Intensive Applications (DIA) is recruiting new industrial doctoral students for our partner companies. The posts are permeated by data-intensive methods such as artificial intelligence and machine learning.

Virtual Manufacturing: Virtual digital twin and digitalization of tools and methods for transforming to Industry 4.0

The vision and goal for Virtual Manufacturing (VM) are to create a digital platform called Virtual digital Twin (VT). The base of the platform will be a digital twin of a factory or office.

The VT platform will be used for improvement projects, maintenance, machine relocations, value stream mapping, tracking system – RTLS, inventory control, monitoring and management of factories. Furthermore, VT is expected to generate benefit for sales, site improvements, factories overview, product development, process development, management monitoring etc. VT will be connected to all kinds of systems and be the holder of information. The goal is also to do the daily work easier by creating methods and tools for digitalization in the transformation to Industry 4.0.

Field of subject for the position: Computer science or mechanical/industrial engineering (with software knowledge), or equivalent.

More information and application

Ankarsrum Electric Motors: Focus on simulation

At Ankarsrum Electric Motors we are continuously developing and optimising new and existing motor types. As we aim to reduce the lead-time and cost for these activities, we are confident that the installation and use of a simulation tool would be instrumental for achievement of these goals.

In the role as a doctoral student at Ankarsrum Electric Motors, you will build our simulation capability and infrastructure in close collaboration with our R&D team members and other key internal and external stakeholders. Your main initial task will be to establish, build and verify viable simulation models for our range of DC, universal and BLDC motors. Secondly, these models will comprise the basis for further optimising efficiency, performance, durability, cost, and sustainability.

The industrial doctotal student will be employed at Ankarsrum Electric Motors in Ankarsrum and enrolled in the graduate program at Linnaeus University. Therefore the position has some academic requirements that needs to be fulfilled.

The industry graduate school DIA

The industry graduate school Data Intensive Applications (DIA) applies academic research to industry challenges. The objective is to develop new knowledge, smarter solutions and innovations in data intensive applications, leveraging on big data, artificial intelligence (AI), and cyber-physical system (CPS) technologies.

To meet these challenges, DIA combines theoretical knowledge from computer science, mechanical engineering and forest technology with practical experience and competences. The industrial doctoral students are employed by our partner companies.

DIA contributes with structured research education and supplies companies with the fundamental competences in big data, AI and CSP, for developing smarter data intensive industry strength systems. DIA also contributes with applied research in co-production with the participating companies and across academic research fields.

The actual research is conducted in individual research projects at the participating companies. These are co-supervised by experts at Linnaeus University and at the partner companies.

The Industry Graduate School, DIA, is closely affiliated to Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) and the complete knowledge environment Smarter Systems funded by the Knowledge Foundation.

Research questions

There are three overarching research questions addressed in DIA. The individual doctoral projects, and the graduate school as a whole, will conduct research to answers these questions.

• How can we utilize abstract digitalization concepts for smarter industrial CPS? How can we build, maintain, and organize open CPS ecosystems?
• How can we verify guarantees of smarter industrial CPS built on data driven models regarding accuracy, performance, response time, safety, etc? How can we assure that they persist over time?
• How can we make data driven models that control smarter industrial CPS self-explainable? How can we convey the knowledge from models to human stakeholders?

Industry objectives

The individual doctoral student projects address three overarching objectives, focusing on the practical relevance for the partner companies.

• To adopt data intensive technologies, such as digital twins and proactive maintenance, for solving concrete problems in the development, maintenance and operation of industrial CPS, and for improving these processes.
• To put together individual solutions and improvements to a common digitalization strategy towards smarter industrial CPS. To define and get started with concrete first pilot projects, set expected benefits, and define a structured systematic roadmap towards digitalization.
• To create new services, platforms and ecosystems supporting smarter industrial CPS.

Industry partners and projects

• Combitech : Change business logic to value-driven models
• Electrolux Professional : Big data exploitation to understand professional products' life cycle
• HL Design : Leveraging Machine Learning for multishop eCommerce platform
• Kuka Nordic :
• SKF : Machine Learning in Manufacturing
• Softwerk : Advanced identification methods for the forest industry through machine learning and AI
• Volvo CE: Predict and verify the products’ performance (2 doctoral students)
• Virtual Manufacturing : Software as a service (SaaS), 3D modelling and twin setup and digitalization for lean production
• Electrolux Professional
• Micropower Group
• Virtual Manufacturing
• Ankarsrum Electric Motors

The courses followed by a * will be offered next academic year as well.

Study period 1 (August–November)

Code transformation and interpretation (5 credits) *.

Duration: August–November Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV507-1.pdf Coordinator:  Jonas Lundberg Application: Send an e-mail to [email protected] Type of course: Foundation

Data mining (5 credits) *

Duration: August–November Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV510-1.pdf Coordinator:  Rafael Messias Martins Application: Send an e-mail to [email protected] Type of course: Data-driven

Information visualization (5 credits) *

Duration: August–November Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV805-1.pdf Coordinator:  Andreas Kerren Application: Send an e-mail to [email protected] Type of course: Data-driven

Project in visualization and data analysis (10 credits) *

Duration: August–January Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV807-1.pdf Coordinator:  Rafael Messias Martins Application: Send an e-mail to [email protected] Type of course: Data-driven

Systems modeling and simulation (5 credits) *

Duration: August–November Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV650-1.pdf Coordinator:  Mauro Caporuscio Application: Send an e-mail to [email protected] Type of course: Cyber-physical systems (CPS)

Engineering Self-Adaptive Software Systems (7,5 credits) *

Duration: September–November Syllabus: https://lnu.se/en/research/PhD-studies/courses/ftk/engineering-self-adaptive-software-systems/ Coordinator:  Danny Weyns Application: Send an e-mail to [email protected] Type of course: Data-driven

Study period 2

Advanced information visualization and application (5 credits) *.

Duration: November–January Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV806-1.pdf Coordinator: Rafael Messias Martins   Application: Send an e-mail to [email protected] Type of course: Data-driven

Formal methods (5 credits) *

Duration: November–January Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4DV701-1.pdf Coordinator: Faiz Ul Muram Application: Send an e-mail to [email protected] Type of course: Foundation

Scientific methods in computer science (5 credits) Duration: November–January Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV502-1.pdf Coordinator: Aris Alissandrakis Application: Send an e-mail to [email protected] Type of course: Foundation

Structural dynamics (7.5 credits)

Duration: November–January Syllabus:  Kursplan.lnu.se/kursplaner/syllabus-4MT315-1.pdf Coordinator:  Andreas Linderholt Application: Send an e-mail to [email protected] Type of course: Cyber-physical systems (CPS)

Study period 3

Machine learning (5 credits) * Duration: January–March Syllabus:  Syllabus-4DV660-1.pdf Coordinator:  Welf Löwe Application: Send an e-mail to [email protected] Type of course: Data-driven

Parallel computing (5 credits) * Duration: January–March Syllabus:  Syllabus-4DV657-1.pdf Coordinator:  Morgan Ericsson Application: Send an e-mail to [email protected] Type of course: Foundation

Project in machine learning (10 credits) * Duration: January–June Syllabus:  https://kursplan.lnu.se/kursplaner/syllabus-4DV652-1.pdf Coordinator:  Welf Löwe Application: Send an e-mail to [email protected] Type of course: Data-driven

Study period 4

Computational and visual network analysis (5 credits) *.

Duration: March–June Syllabus:  https://kursplan.lnu.se/kursplaner/syllabus-4DV809-1.pdf Coordinator: Rafael Messias Martins   Application: Send an e-mail to [email protected] Type of course: Data-driven

Deep machine learning (5 credits) *

Duration: March–June Syllabus:  Syllabus-4DV661-1.pdf Coordinator:  Welf Löwe Application: Send an e-mail to [email protected] Type of course: Data-driven

Sustainable production (7,5 credits) *

Duration: March–June Syllabus:  https://kursplan.lnu.se/kursplaner/kursplan-4MT321-1.1.pdf Coordinator: Jetro Kenneth Pocorni Application: Send an e-mail to Jetro Kenneth Pocorni, [email protected] Type of course: Cyber-physical systems (CPS)

Philosophy of Science for doctoral students (4 credits)

Duration: Maj–June Syllabus: Coordinator: Päivi Jokela Application: Send an e-mail to Päivi Jokela, [email protected] Type of course: Foundation

Using Python for research (5 credits)

Duration: Is offered continuously (individual studies) Syllabus:  https://app.box.com/s/17976bjn7o2o8s103c8ca32aqoj9ntud Coordinator:  Morgan Ericsson Application: Send an e-mail to [email protected] Type of course: Foundation

Presentation and pitching (3 credits)

Duration: Is offered continuously Syllabus: https://lnu.box.com/s/g1c4r38aq65zb8iwede832qp7advhlvr Coordinator: Diana Unander Application: Send an e-mail to [email protected] Type of course: Foundation

Other courses

Seminar series - presentation and participation (4 credits)

Duration: Continuously Syllabus: https://lnu.box.com/s/tqv5jorjd3qmd2esq7wlppkec7dkzpy7 Coordinator: Diana Unander Application: Send an e-mail to [email protected]

Organisation

Steering committee.

Responsible for the strategic governance of DIA:

• Per-Olof Danielsson, Head of Virtual Product Development at Volvo Construction Equipment, chairman
• Dorothee Millon, Field Quality Manager, Electrolux Professional, member
• Torbjörn Danielsson, CEO and Business development, Virtual Manufacturing, member
• Senadin Alisic, Strategy Advisor and Industry PhD student at Combitech Sweden, member
• Åsa Blom, Vice Dean, Faculty of Technology, Linnaeus University, member
• Lars Håkansson, Head of Department, Department of Mechanical Engineering, Linnaeus University, member
• Niklas Malmros, CEO at Sigma Technology Solutions, co-opted member
• Margrethe Hallberg, Digitalization Coordinator for Product Introductions at Scania, co-opted member

Executive board

Responsible for the operational leadership of DIA and for program and research coordination:

• Welf Löwe, Professor in Computer Science, project manager
• Diana Unander, Research and Project Coordinator, project coordinator
• Morgan Ericsson, Associate Professor in Computer Science, program coordinator
• Mauro Caporuscio, Professor in Computer Science, research coordinator

Would your company like to have an industrial doctoral student?

An industrial doctoral student is employed at a company and enrolled as a doctoral student at Linnaeus University. The student combines the regular development work with a research education and gets support from a group of senior researchers at Linnaeus University as supervisors. The research education usually stretches over five year with a set-up of 20 % course work, 60 % research and development at the company and 20 % that the company can use freely.

Do you want to know more? Contact research and project coordinator  Diana Unander .

Publications

• All publications within DIA
• 19 September 2023
• 11 May 2023
• 10 June 2022
• 11 April 2022

Doctoral projects

• Doctoral project: Advanced identification methods for the forest industry through CV/AI The project intends to create opportunities for continued digitalisation in the forest industry. This concerns…
• Doctoral project: AI in administration of agricultural subsidies We want to design, implement and evaluate systems based on artificial intelligence that supports our customer with the administration…
• Doctoral project: Big Data Exploitation for Insight into Electrolux Professional Products Lifecycle Management This doctoral project aims to use Big Data to map the life cycle of professional products…
• Doctoral project: Digital Twin as a Service (DTaaS) This doctoral project targets to work on Digital Twin, with integration to data intensive sources.
• Doctoral project: Digital twin developments within Volvo CE This doctoral project relates to develop a so-called digital twin platform. The aim is to understand customers' problems and support them…
• Doctoral project: Document Classification and Entity Extraction Many aspects of accounting present difficulties in achieving full automation due to the abundance of unstructured information, such as…
• Doctoral project: Ecosystems and smart cities Cities face major climate challenges. In my research, I investigate how digital transformation and ecosystems contribute to increased collaboration…
• Doctoral project: Enhancing MLOps architectures for efficient integration, deployment and inference of AI Models in diverse industrial settings This project focuses on advancing MLOps architectures to…
• Doctoral project: Exploring AI driven operation for forecasting in data-light environments: The multishop concept The advent of big data and AI brought new possibilities to businesses. In this…
• Doctoral project: Machine Learning in Manufacturing Many manufacturing companies struggle with transitioning into the era of smart technologies, due to the fact that modern lab-grown methods and data…

Doctoral students

• dag bjornberg lnu se
• daniel f nilsson lnu se
• +46 470-76 78 65
• +46 73-057 70 64
• diana unander lnu se
• felix viberg lnu se
• gaurav garg lnu se
• joel cramsky lnu se
• kailashchowdary bodduluri lnu se
• manoranjan kumar lnu se
• nemi pelgrom lnu se
• nils johansson lnu se
• rakhshanda jabeen lnu se
• senadin alisic lnu se
• tibojamesliam bruneel lnu se

Affiliated doctoral student

• niels gundermann lnu se

PhD studies at Stockholm University

Stockholm University stands as an attractive destination for those considering PhD studies. With a strong commitment to research excellence and a growing network of international collaborations, the university offers a favourable environment for advanced research and academic growth.

Stockholm University has a long tradition of research excellence spanning various fields of study, both in the humanities, social sciences, and natural sciences. Many faculty members are at the forefront of their respective disciplines, producing influential research that addresses pressing global challenges and contributing to the shaping of industries, policies and societies.

Doctoral students are employees

Moreover, Stockholm University recognizes international collaborations as an essential part of scientific research, thus actively fosters partnerships with esteemed institutions and scholars from around the world. This global perspective enriches the academic experience for PhD candidates, providing valuable insights and opportunities for cross-cultural engagement.

• Stockholm University is one of the 100 highest-ranked universities in the world and one of the top 50 universities in Europe
• We offer a wide range of research subjects within science and human science
• There are no tuition fees for doctoral education in Sweden
• Doctoral students are employees at SU and  receive salary and other standard benefits of employment.

Available PhD positions

As the academia constitutes the cradle of knowledge, I am proud of being part of this community which actively contributes in the generation of innovative ideas which target to solve everyday problems. I also enjoy the close connection to the industry in Sweden.

- Varvara Apostolopoulou Kalkavoura

Read the full interview with Varvara

Doctoral studies in Sweden

Our doctoral studies take four years and result in a degree of Doctor of Philosophy (PhD), the highest academic degree in Sweden. During this period you will both take advanced courses in your field of interest and work in a original research project.  However, after two years, you might have the possibility to take an intermediate degree of licentiate.

Admission requirements

To be admitted to PhD studies, you need to meet the general entry requirements, the specific entry requirements, and have the ability to successfully complete your studies.

General entry requirements include a completed master’s degree from a Swedish university or at least 240 higher education credits including at least 60 at the master’s level, or the corresponding international equivalents.

Institutions may have specific eligibility requirements. Verify the requirements for your area of interest with the relevant institution.

How to apply

One thing that differentiates PhD programmes at Stockholm University and Sweden from those elsewhere is that most of our PhD students are treated like full employees of the university. PhD positions are posted alongside other jobs on our website and applications are made for specific positions. PhD positions can be announced once or twice per year, depending on funding availability.

Before sending your application, you can check the department’s website for further information about the faculty and the possibilities connected with the PhD position. You can also find specific information about the application process in the announce for the position, along with the contact person if you have further questions.

There is usually an opportunity to apply for doctoral studies both in the autumn and spring. On the institutions' websites about doctoral studies, you can find the specific dates applicable to that institution. You can also see the current doctoral positions advertised. 

Find available PhD vacancies at Stockholm University

Funding and financing doctoral studies

In most cases, doctoral students are treated as employees at Stockholm University, which means they receive a salary and other standard benefits of employment.  There is even the possibility for you to extend your period of studies by working with teaching or administrative tasks within your department, up to 20% of a full-time position, which can result in a full extra year of employment as PhD student.

However, it is possible to pursue doctoral studies at Stockholm University also in case you have an external scholarship or special agreement with a company or other external employers. In these special cases, you will be subject to your employer-specific conditions only, and be considered solely a student at Stockholm University.

Residence permits (if applicable)

If you are an international student from a country outside the European Union (EU), European Economic Area (EEA), or Switzerland and you plan to pursue a PhD in Sweden, you will need a residence permit to study in the country. The residence permit is required for stays longer than three months.

You will find more information on the Swedish Migration Agency's website:

Residence permit for doctoral studies

Faculty information

Faculty of science.

Research at the Faculty of Science covers broad knowledge areas, ranging from the elementary particles of atomic nuclei to the outermost limits of the universe, for example. Several of the faculty's institutions have been behind discoveries and scientific breakthroughs that receive recognition worldwide.

The Stockholm University BioResearch School organizes PhD courses for students at any of the Biology departments at SU no matter their subject area.

Stockholm University BioResearch School

Faculty of Humanities

Research in the humanities maintains high quality, with a wide range of subjects, allowing university researchers to engage in collaborative efforts across scientific domains. Philosophy, history, art and literary studies, journalism, linguistics, and Swedish as a second language for the deaf are examples of subjects in which research is conducted.

Those admitted to doctoral education have the opportunity to participate in the Faculty of Humanities Research School.

Faculty of Humanities Research School

Faculty of Law

The Faculty of Law achieves nationally leading and internationally outstanding research. The researcher's freedom characterizes the scientific work. Legal discussions are expected to be lively, open, and ongoing.

Faculty of Social Sciences

The Faculty of Social Sciences conducts qualified and high-quality research within its various established disciplines and interdisciplinary research fields. Strong connections to current challenges for society and politics generate central research questions.

The Faculty of Social Sciences offers doctoral education courses for those admitted to an institution belonging to the Faculty of Social Sciences.

Doctoral Education Courses

Research subjects at Stockholm University

Research infrastructures at Stockholm University

The departments provide education at the PhD level

The individual departments often publish specific information doctoral studies programmes at their respective website.  All institutions offering education at the PhD level are categorized into the following fields: humanities, languages, social sciences, and law, as well as natural sciences.

Department of Archaeology and Classical Studies

• General Archaeology
• Ancient Culture and Society
• Laboratory Archaeology
• Osteoarchaeology

Doctoral studies at the Department of Archaeology and Classical Studies

Department of Asian and Middle Eastern Studies

• Languages and Cultures of Asia
• Languages and Cultures of the Middle East

Doctoral studies at the Department of Asian and Middle Eastern Studies

Department of Culture and Aesthetics

• History of Ideas
• Art History
• Literary Studies
• Theatre Studies
• Media and Communication Studies
• Fashion Studies
• Film Studies

Doctoral studies at the Department of Culture and Aesthetics

Department of History

Doctoral studies at the Department of History

Department of Philosophy

• Practical Philosophy
• Theoretical Philosophy

Doctoral studies at the Department of Philosophy

Department of Teaching and Learning

• Didactics of Mathematics
• Didactics of Natural Sciences
• Language Didactics
• Subject Didactics with a focus on the didactics of aesthetic, humanistic, or social science subjects

Doctoral studies at the Department of Teaching and Learning

Department of English

Doctoral studies at the Department of English

Department of Linguistics

• Linguistics

Doctoral studies at the Department of Linguistics

Department of Romance and Classical Languages

• Romance Languages
• Classical Languages

Doctoral studies at the Department of Romance and Classical Languages

Department of Slavic and Baltic Studies, Finnish, Dutch and German

• Baltic Languages
• Slavic Languages

Doctoral studies at the Department of Slavic and Baltic Studies, Finnish, Dutch and German

Department of Swedish Language and Multilingualism

• Nordic Languages
• Bilingualism
• Translation Studies

Doctoral studies at the Department of Swedish Language and Multilingualism

Social sciences and law

Department of child and youth studies.

• Child and Youth Studies
• Preschool Didactics

Doctoral studies at the Department of Child and Youth Studies

Department of Computer and Systems Sciences

• Computer and Systems Sciences
• Information Society

Doctoral studies at the Department of Computer and Systems Sciences

Department of Criminology

• Criminology

Doctoral studies at the Department of Criminology

Department of Economic History and International Relations

• Economic History
• International Relations

Doctoral studies at the Department of Economic History and International Relations

Department of Economics

Doctoral studies is provided in collaboration with:

Institute for International Economic Studies (IIES)

Swedish institute for social research (sofi).

Programs at the doctoral level at the Department of Economics

Department of Education 

Doctoral studies at the Department of Education

Department of Human Geography

• Geography with a Cultural Geography focus

Doctoral studies at the Department of Human Geography

Department of Political Science

• Political Science

Doctoral studies at the Department of Political Science

Department of Psychology

Doctoral studies at the Department of Psychology

Department of Public Health Sciences

• Public Health Sciences

Doctoral studies at the Department of Public Health Science

Department of Social Anthropology

• Social Anthropology

Doctoral studies at the Department of Social Anthropology

Department of Sociology

• Sociological Demography

Studies at the doctoral level at the Department of Sociology

Department of Social Work

• Social Work

Doctoral studies at the Department of Social Work

Department of Special Education

• Special Education

Doctoral studies at the Department of Special Education

Department of Statistics

Doctoral studies at the Department of Statistics

Doctoral studies at the Department of Teaching and Learnin

• International Economics
• Research and higher education in economics

Doctoral studies in Economics

Stockholm Business School

• Business Administration
• Financial Economics

Doctoral studies at Stockholm Business School

• Doctoral candidates at SOFI are enrolled in either the Department of Sociology or the Department of Economics.

Doctoral studies at the Swedish Institute for Social Research

Department of Law

• Jurisprudence

Doctoral studies at the Department of Law  

Natural Sciences

Department of ecology, environment and plant sciences.

• Ecology and Evolution
• Ecotoxicology
• Marine Biology
• Plant Physiology
• Plant Systematics

Doctoral studies at the Department of Ecology, Environment and Plant Sciences  

Department of Molecular Biosciences, Wenner-Gren Institute

• Molecular Biosciences

Doctoral studies at the Department of Molecular Biosciences, Wenner-Gren Institute

Department of Zoology

• Functional Zoomorphology
• Population Genetics
• Zoological Ecology
• Zoological Systematics and Evolution

Doctoral studies at the Department of Zoology

Stockholm Resilience Centre

• Sustainability Science

Doctoral studies at Stockholm Resilience Centre

Earth and Environmental Sciences

Department of geological sciences.

• Geochemistry
• Marine Geology

Doctoral studies at the Department of Geological Sciences  

Department of Environmental Science

• Environmental Science

Doctoral studies at the Department of Environmental Science

Department of Physical Geography

• Physical Geography

Doctoral studies at the Department of Physical Geography

Department of Biochemistry and Biophysics Biophysics

• Biochemistry
• Biochemistry towards bioinformatics

Doctoral studies at the Department of Biochemistry and Biophysics  

Department of Materials and Environmental Chemistry

• Analytical Chemistry
• Physical Chemistry
• Materials Chemistry
• Neurochemistry with Molecular biology
• Inorganic Chemistry

Doctoral studies at the Department of Materials and Environmental Chemistry  

Department of Organic Chemistry

• Organic Chemistry

Doctoral studies at the Department of Organic Chemistry

Math/Physics

Department of physics.

• Chemical Physics
• Medical Radiation Physics
• Theoretical Physics

Doctoral studies at the Department of Physics  

Department of Astronomy

Doctoral studies at the Department of Astronomy  

Department of Mathematics

• Computational Mathematics
• Mathematics
• Mathematical Statistics

Doctoral studies at the Department of Mathematics

Department of Meteorology

• Atmospheric Science and Oceanography

Doctoral studies at the Department of Meteorology

For a general study plan in any natural science subject, please contact  [email protected].

The graduation ceremony

Every year, over 200 PhD students defend their thesis at Stockholm Universit, and get a chance to celebrate their achievement at the Stockholm City Hall.

Opportunities abroad for PhD students

There are several ways to participate in international mobility experiences during your PhD studies.

Find possible exchange opportunities for PhD students here

Available PhD Student Positions

On the employee web portal PhD students will find more detailed information about the dissertation defence process .

Last updated: February 6, 2024

Source: Offices of Human Science and Science, Communications Office and Student Services

Lund University

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Doctoral studies

Start your academic career with us and make a difference

World-class research, outstanding research staff and specialised research environments at Lund University create excellent conditions for doctoral students.

As a doctoral student at Lund University, you will not only be well prepared for a successful career as an independent researcher, but you will also have the possibility to publish your work independently during your studies and to gain significant teaching experience.

• Lund University offers doctoral education in all nine faculties
• There are no tuition fees for doctoral education at Lund University
• You apply directly to the relevant faculty/department when they advertise a doctoral position
• Self-funded doctoral students should contact the department of their research interest directly.

Admission requirements

To be admitted to a doctoral programme, you need to have completed courses of at least 240 credits (of which at least 60 credits must be for Master’s level studies) or acquired equivalent knowledge in some other way.

In most cases, students will hold a Bachelor’s degree and a Master’s degree, with a major in the same subject as the intended postgraduate study. The major must include a degree thesis presenting the results of independent research.

The quality of your thesis/theses is of particular importance and it is important that you demonstrate a capacity for independent thinking in this piece of work.

Students must have a very good command of English and you may be asked to include proof of proficiency in the form of a TOEFL or IELTS test, if requested by the individual department.

Admission rules for doctoral education at Lund University | 2022 (PDF 249 kB, new tab)

Applying for a doctoral (PhD) position

Doctoral education is organised at the faculty or department level. Application procedures and periods differ between faculties and departments. While some of them advertise their doctoral positions around the same time every year, mostly in spring, others advertise their positions on an ongoing basis.

You apply for a doctoral education position within a certain subject area. Admission to a doctoral education position is restricted and competition for advertised positions is usually tough.

Finding a suitable position

Any funded doctoral positions at Lund University are advertised on our vacancies page on this website (see link below). Select the category 'Doctoral students'.

Once you have found a position you are interested in, carefully read through the vacancy announcement to find out whether you meet the requirements.

Before you apply, we recommend that you also check the relevant faculty's or department's website for any additional information about the application process.

Should you have any questions about a specific position, please contact the contact person(s) listed in the vacancy announcement.

Find PhD vacancies

How to apply

To apply for a doctoral position, you must create an account in the recruitment system Varbi.

Follow the instructions regarding application documents and procedure in the vacancy announcement.

The application documents that you submit through Varbi, including any supporting documents, are sent directly to the faculty or department that advertised the position. 

If you are self-funded through external scholarships or funding, you do not need to create an account in Varbi. You should instead make direct contact with the relevant faculty or department. 

Applying for a position

Employment as a doctoral student

As a general rule, a person admitted to doctoral studies at Lund University is employed through a doctoral studentship.

If you have a doctoral studentship, you are considered to be both a student and an employee with a salary during your doctoral studies. As an employed doctoral student, you are covered by local and central agreements concerning your terms of employment.

In parallel to the doctoral education programme, as a doctoral student you may work with administrative and teaching duties (no more than 20% of a full-time position).

Lund University is responsible for education-related matters and the study environment regardless of how you finance your doctoral studies. All doctoral students also have access to the University’s Occupational Health Service.

Other forms of financing

It is possible to undertake your studies using another form of financing, such as an external scholarship or an agreement between Lund University and an external employer.

Financed by external employer

If you are admitted to doctoral studies and have a position with an employer other than Lund University, you are solely a student, and it is the employer who is responsible for employer-related matters.

External scholarships

As a doctoral student with a scholarship, you are solely a student. You are therefore not entitled to contractual employment benefits at Lund University.

In some cases, a doctoral student with a scholarship is entitled to apply for a doctoral studentship when three years remain of the third-cycle studies.

Doctoral studies – faculty information

On the faculties' webpages for prospective doctoral students you can get specific and detailed information application procedures, admission requirements for various subjects, study plans and more.

• Faculty of Engineering (LTH)
• Faculty of Fine and Performing Arts
• Faculties of Humanities and Theology
• Faculty of Law
• Faculty of Medicine
• Faculty of Science
• Faculty of Social Sciences
• School of Economics and Management (LUSEM)

Residence permits (if applicable)

If you require a residence permit, you can only complete your permit application after you receive a letter of acceptance from Lund University. Residence permit applications are dealt with by the Swedish Migration Agency.

Resi­dence permit for doctoral studies – migrationsverket.se

About doctoral studies

A doctoral programme consists of 240 ECTS credits and normally requires four years of full-time study. The programme concludes with a doctoral thesis of at least 120 credits.

A 'licentiate' degree is equivalent to half the coursework required for a full doctoral programme and a licentiate thesis of at least 60 credits. It is equivalent to the MPhil of the British education system.

The following links are to the Swedish Council for Higher Education website:

• Degree of Doctor
• Degree of Doctor in the fine, applied and performing arts 
• Degree of Licentiate 
• Degree of Licentiate in the fine, applied and performing arts

Related links

• Institutes and research centres
• Research excellence areas
• Find research at Lund University
• Services for LU researchers

Doctoral student vacancies

Check out our vacancies page regularly to find open doctoral positions.

Doctoral conferment ceremony

The ceremonial highlight of the academic year.

Postgraduate research schools

Additional development opportunities for doctoral students and postdocs.

Call for Academic PhD Projects in Data-driven Life Science (Call closed)

Generic description of the ddls phd program.

The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-year initiative funded with a total of 3,1 billion SEK from the Knut and Alice Wallenberg Foundation. The purpose of the program is to recruit and train the next-generation of data-driven life scientists and to create globally leading computational and data science capabilities in life science in Sweden.

One part of the program is to establish a research school for 260 PhDs, within both academia and industry. The aim of the DDLS Research School is to educate highly skilled and competent professionals who will make a significant contribution to the field of life science research in Sweden.

The PhD students will be recruited to a host university/organization in Sweden, aiming to link them up with strong local research environments as well as with the national DDLS program.

As the PhD candidates are recruited at a host university in Sweden, they will be enrolled as members of the DDLS Research School and expected to take part in the DDLS Research School activities (networking events, courses, scientific visits, etc.).

The DDLS program will focus on four strategic areas of data-driven research: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection.

We are now launching a competitive grant call for group leaders (and hence potential PhD student supervisors) to suggest exciting data-driven research projects and training opportunities for PhD students in the four strategic areas of data-driven life science. In this call, 20 academic PhD projects will be awarded.

What is Data-driven Life Science?

Data-driven life science is a field of research that focuses on using data, computational methods and artificial intelligence to study biological systems and processes. This approach can include assembling, sharing, integration and advanced analysis of large amounts of data from diverse sources, including experiments, observations, and simulations, in order to gain a better understanding of how living organisms function.

For a PhD project to be considered data-driven it has to have a clear data science component such as the use of advanced data analysis techniques, from statistics to machine learning involving either method development or novel application of data science methods to life science problems. Projects that only involve laboratory research or that depend solely on the acquisition of large amounts of new biological data from e.g. laboratory experiments or patient materials will not be given priority. However, laboratory research to validate and extend data-driven insights can be included.

Role of the DDLS Research School

The focus of the research school is to engage the students with a national network and in annual network activities. The DDLS Research School will complement already existing graduate school and other training activities at universities, and it does not represent a full standalone national PhD training program. Specific national DDLS Research School courses will be provided at a national to assure that the students will at the end of their education have proficiency in data handling and analysis, integration of multidisciplinary knowledge and ethics.

The supervisors of the DDLS PhD students are expected to be active in organizing and contributing course material and training events of the DDLS Research School. The students will also have access to a plethora of other training and courses arranged by the SciLifeLab Training Hub.

Requirements

Project proposals in the four DDLS research areas are welcome. The funded projects should be in line with DDLS strategy and have a novel and original data-driven perspective, be of high scientific quality and combine the life science and data science topics. Project applicant, i.e. the main supervisor of the PhD student must have a secured employment at a Swedish University during the suggested PhD period. The call is open to all researchers in Sweden who can act as a main supervisor or a co-supervisor for a PhD student. One applicant can be the main supervisor in only one academic and one industrial project, but can act as co-supervisors in applications by other PIs. DDLS fellows who already have a start-up package with PhD student positions, cannot apply to the call as main supervisor for DDLS academic PhD student positions. However, they can file joint applications as co-supervisor together with e.g. SciLifeLab, WCMM fellows or any other scientists with the goal of forming new joint collaborations and creating new multi-disciplinary data-driven opportunities for training PhD students.

Project participants (supervisors and students) are expected to be active contributors and participants in the national DDLS community events, training activities, seminars, and symposia organized by the DDLS Research School.

Any necessary co-funding needed for a KAW-funded project is the responsibility of each university/ department /PI and should be ensured via a letter of commitment from the head of the department/faculty.

Required Documents

• Letter of commitment from the Head of Department/faculty
• CV of the suggested main supervisor (max 2 pages + 1 page Top 10 publications)
• CV of the co-supervisor(s) (if applicable) (1 page CV)
• Research project proposal: Up to three pages. Describe the field of research and the central questions, include the specific aims of the suggested PhD project, the material and methods and data analysis and computational approaches as well as the contributions from the team of supervisors. Indicate what additional costs are encountered in the project and how such costs will be covered from other funding sources. Explain the training plan for the PhD student. Indicate why this project and the PI and the co-PIs are ideal to promote the goals of the DDLS and the specific DDLS research area, both in terms of research questions and the training of next-generation life scientists.
• 1 page, including the following: what is the local research environment of the PI (laboratory, department, faculty, university) and how this links to the DDLS program goals and provides a good training environment with sufficient critical mass of expertise. Describe how the team of supervisors will collaborate. Describe any local graduate school/doctoral program the student will be affiliated with and how this could provide synergistic benefits.

Evaluation Process and Decisions

Project proposals and PIs are evaluated and ranked by international reviewers. Projects are evaluated according to defined DDLS evaluation criteria :

• Scientific quality of the project
• Merits of the applicant and co-applicants supervisor/co-supervisor (scientific and training)
• Fit and contribution of the project to the DDLS program
• Quality of the supervision plan and the training environment

We will also consider inclusiveness and diversity.

The projects will first be screened for eligibility, and then sent to an international evaluation committee. Initially, the plan was for the DDLS Steering Group to assess the projects after the pre-screening process. Since conflicts of interest emerged for some of the Steering Group members, the group is no longer part of the evaluation process. The final decisions will be made by the SciLifeLab National Board. We will select about 20 projects, as well as a few backup projects. As we expect a very large number of applications, we are not able to provide detailed written feedback on each application. The accepted supervisor(s) are then eligible to be part in the next step, the selection of the PhD students as part of an international call. The supervisors(s) are then also invited to take part in the activities of the DDLS program, the DDLS Research School and contribute to associated training events. The next steps in the process will be refined later on, but are described here for completeness.

PhD Student Selection: International Announcement of All Positions

After the board decision is done, SciLifeLab will coordinate an international joint announcement for all DDLS PhD positions. This will be done jointly together with all DDLS partner organizations that have been granted a PhD slot. The announcement will be posted in relevant major international sites, and includes a short description of the DDLS program and

the four DDLS research areas, information onthe positions at each university, a short description of each of the project and the associated PIs (affiliation, research area, website), desired skills and qualifications of the candidates as well as any specific requirements of the host graduate school. The details will be available via links to the SciLifeLab web site and local sites. Each university with a granted project can also announce the position at their respective websites to meet the local rules and regulations. The local announcement should clearly indicate that the PhD position is part of a large national DDLS program.

Application Process and Requirements for the PhD students

The PhD student candidates will submit their applications to the respective university, according to the local application instructions. The candidates can apply to multiple positions but will need to file an application separately at each university.

Required documents should include any documents typically required by the respective university, including a detailed description of the motivation, track-record and potential of the candidate in data-driven life science.

Evaluation of the PhD Candidates

All PIs with approved projects select their PhD candidates from the international joint PhD call according to the timelines set by the DDLS program. The purpose of the joint timelines is to promote the national aspect of the program.

During the recruitment process at each local department with granted PIs, a DDLS representative is recommended to be present at the interviews but they will not participate in the scoring and selection of the candidates. The role of the DDLS representative is only to ensure that the candidates from the international search are fairly considered and meet the overall requirements of the DDLS program and the DDLS research school. If the DDLS representative cannot be part of the committee, a one-page description of the selection process, including a summary of the main applicants from the international call is requested by the DDLS office. All the selected candidates are at this stage approved by the DDLS Program Director and are eligible for funding.

Conditions for Funding

The DDLS program finances both 4-year full-time PhD positions as well as positions for 5 years at an 80% effort. It is currently expected that 20 projects / PhD positions in academia will be awarded in the first round of the recruitment. The PIs of the approved projects, together with the head of department, will be asked to sign an agreement containing the Terms and Conditions of the suggested DDLS funding. The granted funds will not be available until the PhD student is recruited by the department/university. The incurred project costs will be requisitioned according to the funding conditions for all DDLS activities.

Financial Information

• The grants will be funded by KAW. The PIs, departments or faculties are responsible for any necessary co-funding needed at each university.
• 3,25 MSEK total KAW funding per project.
• Out of the total KAW funding, max. 165 KSEK can be allocated for running costs during the project period defined.
• A maximum of 18% of the amount granted by KAW can be allocated for premises and overhead costs.
• There is also a maximum coverage of 50% for LKP (payroll overhead) on personnel costs.
• Costs will be reimbursed by requisition to KAW. KTH/SciLifeLab will coordinate this process. Information about the financial process flow and reporting templates will be provided for this purpose at a later stage.
• No funding can be directed to industry, industrial partners or other public sectors in this call.

Application deadline

January 10 2024, 15:00 CET

Timelines for Selection of Projects (November 2023 – March 2024) – Updated

• Call for projects (November 24-January 10)
• Evaluation by DDLS (January)
• List of selected projects ( end of March – updated )

Tentative timelines for Selection of PhD Students – Updated

• Announcement of the positions (April 2024)
• Evaluation of the applications (April – May)
• Interviews (end of May)
• List of selected PhD students – offer and acceptance (June)
• Start of the individual projects (August – October or upon agreement between PI and PhD student, preferably not later than October)

For questions, please contact [email protected]

Last updated: 2024-03-19

Content Responsible: Johan Inganni( [email protected] )

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MSc Biostatistics and Data Science

The master's programme in Biostatistics and Data Science offers a unique combination of Biostatistics and Data Science. Students with backgrounds in Mathematics, Statistics, Computer Science, or related disciplines, are provided with the skills to solve problems in Biology, Medicine, and Public Health. The programme is offered jointly by KTH, Karolinska Institutet and Stockholm University.

Application deadlines for studies starting 2024

16 October (2023): Application opens 15 January: Last day to apply 1 February:  Submit documents and, if required, pay application fee 21 March:  Admission results announced August: Arrival and study start

Next application round

Application for studies starting next year opens in October. Subscribe to our newsletter and we'll remind you when it opens.

Biostatistics and Data Science at KTH, Karolinska Institute and Stockholm University

In this two-year master's programme, you will learn the key skills required to work as a biostatistician or data scientist. You will get training in statistics, computational science, and programming, along with the theoretical and practical education to apply these skills to challenges in biology, medicine, and public health. In addition, you will learn the analytic techniques used in data science to prepare you for the data-driven challenges of modern medical research and a career as a data scientist. During your two years in Stockholm, you will study a tailored selection of courses from three universities with prominent research in their respective fields: Karolinska Institutet, KTH Royal Institute of Technology, and Stockholm University.

The programme is delivered by a team of teachers who are internationally recognised for their research in developing new biostatistics and data science methods, and for their collaborative research with scientists from other disciplines to improve health outcomes.

The first year begins with rigorous courses in statistics, exploring probability theory and statistical inference, and statistical modelling with a heavy focus on modelling biomedical data. Additionally, you will get an introduction to computer intensive methods in mathematical statistics. These courses give you the basis for elective courses in machine learning and statistical learning that you will take in the second half of the year. You will also get an introduction to human biology, physiology, and genetics, and an introduction to medical research, emphasising its multidisciplinary nature and the role of biostatistics and data science in medical research and society. 

The second year introduces topics in biostatistical science that complicate or extend the concepts and methods covered in previous courses, for example, incomplete or missing data, correlated or clustered data, and Bayesian inference. Courses in the second year build upon previous courses by giving an overview of methods for designing and analysing medical research studies in three areas: pre-clinical studies and animal research, clinical trials, and observational studies.

In the final semester, you will conduct a degree project, which involves participating in research projects in an academic or industrial environment.

Programme Presentation

In this recording from October 2023, you will learn about the master's programme in Biostatistics and Data Science. The programme directors, Jimmy Olson from KTH and Therese Andersson from Karolinska Institute, host the webinar.

New programme

This is a new programme with the first students starting in the autumn of 2024. In an article about the programme lauch KTH Professor and programme director Jimmy Olsson said: “The three different universities bring complementary expertise, KI with strong research in biostatistics and medicine, KTH in computational statistics, machine learning, and image analysis and SU in mathematical statistics, infectious disease modeling and population genetics.”

• Read the news story about the programme launch

The combination of biostatistics and data science gives graduates an excellent profile for challenging and rewarding careers in industry (for example, biomedical, healthcare, insurance, and pharmaceutical sectors), government (for example, public health agencies) and academia. There is a shortage of trained biostatisticians and data science professionals, both in Sweden and internationally. After graduating from this programme, you will find excellent opportunities for doctoral studies, both in developing new biostatistics and data science methods and applying your knowledge and skills in biostatistics and data science to address research topics in biology, medicine, and public health.

• Virtual campus tour

Siwat from Thailand is a student at the School of Engineering Sciences (at KTH). In the virtual tour he and some other KTH students will show you around the campuses.

• Take the full campus tour

Through our newsletter you will receive important real-time information to make your road to KTH as smooth as possible.

Explore KTH

Visit our campuses through an immersive digital tour where our students guide you through their favourit KTH spots.

Whether you have questions about studies at KTH or student life in Stockholm we will get you the information you need.

• Ask us about studies

Biostatistics and Data Science

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• How to apply
• Entry requirements
• Scholarships
• Why choose KTH?

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42 PhD jobs in Sweden

Find PhD jobs in Sweden here. To have jobs sent to you the day they're posted, sign up for job alerts.

• PhD positions in Stockholm (24)
• PhD positions in Karlstad (8)
• PhD positions in Gothenburg (6)
• PhD positions in Umeå (1)
• PhD positions in Jönköping (1)

Other countries

• PhD positions in Belgium (135)
• PhD positions in Netherlands (104)
• PhD positions in Switzerland (50)
• PhD positions in France (42)
• PhD positions in Finland (38)

Search results (42)

Ph.D. in wireless sub-THz and THz communications for 6G and beyond

Project descriptionWireless technology has evolved over five generations, but the need for higher efficiency and reliability is never-ending. The 6G research goal is to identify new groundbreaking ...

Doctoral student with focus on combustion modelling in battery systems during thermal runaway

Lund University, Faculty of Engineering, LTH, Department of Energy SciencesLund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based i...

Doctoral (PhD) student position in DNA-based digital data storage

Do you want to contribute to top quality medical research? To be a doctoral student means to devote oneself to a research project under supervision of experienced researchers and following an indiv...

Doctoral students in industrial product realisation

Join the research profile GRACE aiming at accelerating the green transition in the manufacturing industry! We like to welcome you to Jönköping University, Shool of Engineering and our department of...

PhD Student in Statistical Physics with focus on active matter

The Department of Physics at the University of Gothenburg is located in the center of Gothenburg, with approximately 100 employees. The communication routes are good both nationally and internationally. The research focuses within the fields of At...

Doctoral Student in Business Administration (Service Design and Sustainability), based at CTF

DescriptionThe Faculty of Arts and Social Sciences is accepting applications for a doctoral position leading to a PhD in Business Administration with a specialisation in service design for circular...

Doctoral student in Computer Science with a specialisation in Beyond-5G (B5G) and 6G mobile systems

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Data Science & AI

Syllabi for MS/PhD Interview & Entrance Test

The written test will have two parts:

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

Theory Syllabus

Probability and statistics.

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

Linear Algebra

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

Calculus and Optimization

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

Programming, Data Structures and Algorithms

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

Coding Syllabus

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

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

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Interview Topics

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

For MS/PhD Interviews

Machine learning.

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

Artificial Intelligence (AI)

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

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

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Global research team finds no clear link between maternal diabetes during pregnancy and ADHD in children

An international research team led by Professor Ian Wong Chi-kei, Head of the Department of Pharmacology and Pharmacy at LKS Faculty of Medicine of the University of Hong Kong (HKUMed) has just provided valuable evidence through a 20-year longitudinal study to address the longstanding debate concerning the potential impact of maternal diabetes on attention-deficit/hyperactivity disorder (ADHD) in children. This study, analysing real-world data from over 3.6 million mother-baby pairs in China's Hong Kong, Taiwan, New Zealand, Finland, Iceland, Norway and Sweden, showed that maternal diabetes during pregnancy is unlikely to be a direct cause of ADHD. The findings of this groundbreaking study were published today (8 April) in Nature Medicine .

Globally, approximately 16% of women have high blood sugar levels during pregnancy, and the prevalence of diabetes during pregnancy has been on the rise owing to factors like obesity and older maternal age. This can negatively affect the baby's brain and nervous system development. ADHD is one of the most common neurodevelopmental disorders in children, which can have severe negative consequences. Individuals with ADHD are prone to poor outcomes such as emotional problems, self-harm, substance misuse, educational underachievement, exclusion from school, difficulties in employment and relationships, and even criminality.

The impact of maternal diabetes on the risk of ADHD in children has been a subject of debate because of inconsistent findings in previous studies. As a result, concerns regarding pregnancies in women with diabetes and the potential connection to the risk of ADHD in children have persisted. Recognising the importance of identifying risk factors for ADHD, especially for women of childbearing age, the cross-regional study, funded by the Hong Kong Research Grants Council, utilised population-based data from China's Hong Kong, Taiwan, New Zealand, Finland, Iceland, Norway and Sweden to comprehensively assess the association between maternal diabetes and the risk of ADHD in offspring.

Research methods and findings

This extensive study, which included a remarkable sample size of over 3.6 million mother-child pairs from 2001 to 2014, with follow-up until 2020, yielded crucial observations regarding the association between maternal diabetes during pregnancy and the risk of ADHD. The research team first found that children born to mothers with any type of diabetes, whether before or during pregnancy, had a slightly higher risk of ADHD compared to unexposed children, with a hazard ratio of 1.16. The study further identified elevated risks of ADHD for both gestational diabetes (diabetes during pregnancy) and pregestational diabetes (diabetes before pregnancy). The hazard ratio for gestational diabetes was 1.10, indicating a modestly increased risk, whereas the hazard ratio for pregestational diabetes was 1.39, suggesting a more substantial association.

However, an intriguing finding emerged when the research team compared the risk of ADHD between siblings with discordant exposure to gestational diabetes and found no significant difference. This unexpected result indicates that the previously identified risk of ADHD when children were exposed to gestational diabetes during pregnancy is likely due to shared genetic and familial factors, rather than gestational diabetes per se. These findings challenge previous studies that suggested maternal diabetes during or before pregnancy could heighten the risk of ADHD in children.

Research significance

According to Professor Ian Wong Chi-kei, Lo Shiu Kwan Kan Po Ling Professor in Pharmacy, and Head of the Department of Pharmacology and Pharmacy, HKUMed, the process of coordinating with renowned scholars from around the world analysing cross-regional cases spanning over 20 years was no mean feat. This collaborative effort aimed to establish a comprehensive understanding of the matter at hand.

'In contrast to previous studies, which hypothesised that maternal diabetes during pregnancy could significantly increase the risk of ADHD, our study found only a modest association between maternal diabetes and ADHD in children after considering the intricate interplay of various influential factors. Notably, sibling comparisons showed this association is likely influenced by shared genetic and familial factors, particularly in the case of gestational diabetes,' explained Professor Wong.

He highlighted the need for deliberate consideration and future research. 'This implies that women who are planning pregnancy should look at their holistic risk profile rather than focusing solely on gestational diabetes,' he said. 'Moving forward, it is crucial for future research to investigate the specific roles of genetic factors and proper blood sugar control during different stages of embryonic brain development in humans.

About the research team

The research was jointly led by Professor Ian Wong Chi-kei, Lo Shiu Kwan Kan Po Ling Professor in Pharmacy, and Head of the Department of Pharmacology and Pharmacy, HKUMed; Dr Kenneth Man Keng-cheung, Honorary Assistant Professor of the Department of Pharmacology and Pharmacy, HKUMed, and Lecturer of the School of Pharmacy, University College London; Dr Carolyn Cesta, Assistant Professor of the Centre for Pharmacoepidemiology, Karolinska Institute, Sweden; Professor Edward Lai Chia-cheng, School of Pharmacy, National Cheng Kung University, Taiwan; Professor Helga Zoega, Associate Professor of the School of Population Health, Faculty of Medicine and Health, UNSW Sydney, Australia. The first authors were Dr Adrienne Chan Yu-ling, Senior Research Assistant, and Dr Gao Le, Postdoctoral Fellow, Department of Pharmacology and Pharmacy, HKUMed; Dr Miyuki Hsieh Hsing-chun, School of Pharmacy, National Cheng Kung University, Taiwan; and Dr Lars Kjerpeseth, Department of Chronic Diseases, Norwegian Institute of Public Health.

Other members in the research team included experts in child psychiatry and epidemiology. They were Dr Raquel Avelar; Professor Tobias Banaschewski; Dr Amy Chan Hai-yan; Professor David Coghill; Dr Jacqueline M Cohen; Dr Mika Gissler; Professor Jeff Harrison; Professor Patrick Ip Pak-keung, Clinical Professor, Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, HKUMed; Dr Øystein Karlstad; Dr Wallis CY Lau, Honorary Research Associate, Department of Pharmacology and Pharmacy, HKUMed; Dr Maarit K Leinonen; Dr Leung Wing-cheong; Liao Tzu-chi; Dr Johan Reutfors; Dr Shao Shih-chieh; Professor Emily Simonoff; Professor Kathryn Tan Choon-beng, Department of Medicine, School of Clinical Medicine, HKUMed; Professor Katja Taxis; and Andrew Tomlin.

Acknowledgements

This work was supported by the General Research Fund of the Hong Kong Research Grants Council.

• Attention Deficit Disorder
• Diseases and Conditions
• Mental Health Research
• Pregnancy and Childbirth
• Chronic Illness
• Personalized Medicine
• Children's Health
• Attention-deficit hyperactivity disorder
• Methylphenidate
• Adult attention-deficit disorder
• Diabetes mellitus type 1
• Maternal bond
• Diabetes mellitus type 2
• Amphetamine

Story Source:

Materials provided by The University of Hong Kong . Note: Content may be edited for style and length.

Journal Reference :

• Adrienne Y. L. Chan, Le Gao, Miyuki Hsing-Chun Hsieh, Lars J. Kjerpeseth, Raquel Avelar, Tobias Banaschewski, Amy Hai Yan Chan, David Coghill, Jacqueline M. Cohen, Mika Gissler, Jeff Harrison, Patrick Ip, Øystein Karlstad, Wallis C. Y. Lau, Maarit K. Leinonen, Wing Cheong Leung, Tzu-Chi Liao, Johan Reutfors, Shih-Chieh Shao, Emily Simonoff, Kathryn Choon Beng Tan, Katja Taxis, Andrew Tomlin, Carolyn E. Cesta, Edward Chia-Cheng Lai, Helga Zoega, Kenneth K. C. Man, Ian C. K. Wong. Maternal diabetes and risk of attention-deficit/hyperactivity disorder in offspring in a multinational cohort of 3.6 million mother–child pairs . Nature Medicine , 2024; DOI: 10.1038/s41591-024-02917-8

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