PhD student in Machine Learning with a focus on Bayesian models and methods for biomedicine

Are you interested in working with Bayesian mathematical models and machine learning methods for biomedicine and health, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favorable working conditions? We welcome you to apply for a PhD position at Uppsala University.

Uppsala University has a long tradition of successful research – among its alumni are 16 Nobel Prize laureates, including, most recently, Svante Pääbo. The University is unique when it comes to combining IT with wider research, from life sciences to the humanities, and this collaboration is currently facilitated by AI4Research and the Centre for Interdisciplinary Mathematics.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, have around 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website.

At the Division of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical systems, neuroscience, and safety and security. The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Cambridge, University of Oxford, Imperial College, University of Sydney, University of Newcastle and Aalto University. We strive for all PhD students to get a solid international experience during their PhD.

The position is part of the national research program WASP.  

Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry. Read more: https://wasp-sweden.org/

The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/

Project description
With the recent surge in biomedical data, there exists a need for new mathematical models and machine learning methods that can extract as much information as possible from these data. Advances in computational Bayesian statistics have catalysed the applicability of probabilistic programming frameworks (PPFs) such as Stan, Turing, and Pyro in biomedical research. However, PPFs remain largely underused in biomedical pipelines. This underuse is partly due to a strong tradition in the biomedical community to work with frequentist statistics, and partly due to the fact that life-science data often present with a set of technical challenges that complicate the practical implementation of Bayesian models and methods. This project aims to address these challenges and facilitate the use of Bayesian methods in biomedical research. The selected PhD student will work with Bayesian modelling of life-science systems, computational Bayesian inference with e.g., Hamiltonian Monte Carlo sampling, and Bayesian design of experiments. The project will be supervised by Sara Hamis (Uppsala University) and Eszter Lakatos (Chalmers University of Technology). The PhD student will be located at Uppsala University.

Duties
The doctoral student will primarily devote their time to graduate education. Other departmental duties of at most 20%, including teaching and administration, may also be included in the employment.

Requirements
Entry requirements for doctoral education are regulated in the Higher Education Ordinance. To meet the general entry requirements for doctoral studies, you must:

• hold a Master’s (second-cycle) degree in engineering physics, applied mathematics, physics, computer science, machine learning, or in a similar research field, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

The University may permit an exemption from the general entry requirements for an individual applicant, if there are special grounds (Chapter 7, § 39 of the Higher Education Ordinance). For special entry requirements, please see the subject’s general study plan. 

We are looking for candidates with:

• a strong interest in developing Bayesian mathematical models and machine learning methods for biomedical and health research,
• good communication skills and sufficient proficiency in oral and written English,
• programming experience,
• creativity, thoroughness, and a structured approach to problem-solving,
• a collaborative mindset and enthusiasm for interdisciplinary work.

Additional qualifications
Experience with calculus, linear algebra and programming is required. Prior knowledge of biology or medicine is not required. Experience with one or more of the following subjects is meriting:

• mathematical biology,
• Bayesian statistics,
• Hamiltonian Monte Carlo,
• machine learning,
• pharmacokinetics and pharmacodynamics.

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.

Application
The application must include:

1. a cover letter (at most 1 page) outlining the applicant’s motivation for applying for this position, including a self-assessment on why you would be the right candidate for this position, and your expected earliest starting date (a starting date between August and December 2025 is expected);
2. a CV;
3. degrees and transcript of records with grades (translated to English or Swedish);
4. the Master’s thesis (or a draft thereof, and/or some other self-produced technical or scientific text), publications, and other relevant documents;
5. references with contact information (names, emails and telephone number) and up to two letters of recommendation.

Applicants who fulfill at least one of the eligibility requirements are strongly encouraged to apply. All applicants should indicate their earliest possible start date.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date August 18, 2025 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Assistant Professor Sara Hamis, email: sara.hamis@it.uu.se. 

Please submit your application by May 23, 2025, UFV-PA 2025/1326.

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