Postdoctoral position in Machine Learning Theory

We seek a highly motivated Postdoctoral Researcher to join us at the Department of Information Technology, in exploring fundamental questions at the intersection of theory and real‑world learning systems. You will combine rigorous theoretical analysis (e.g., wrt robustness, optimization, Bayesian methods) with real‑world challenges in drug discovery, precision medicine, and systems biology, ensuring your insights deepen the foundations of ML, and also translate to pressing scientific problems.

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.

The position is hosted by the Division of Scientific Computing (TDB) within the Department of Information Technology. As one of the world’s largest focused research environments in Computational Science, the research and education has a unique breadth, with large activities in areas such as mathematical modeling, development and analysis of algorithms, machine learning theory, optimization, scientific software development and high-performance computing.

The division is also an important part of the eSSENCE strategic collaboration on e-science, and the Science for Life Laboratory (SciLifeLab) network. SciLifeLab is a leading institution and national research infrastructure with a mandate to enable cutting-edge life sciences research in Sweden, foster international collaborations, and attract and retain knowledge and talent. The successful candidate will be hosted by the Scientific Machine Learning group at TDB and SciLifeLab. The group specializes in developing theory, methods and software for enabling data driven science. Relevant to this position, the group is active in exploring machine learning theory, large-scale optimization, Bayesian inference and uncertainty-aware learning, and other aspects of statistical learning. We have a wide network of collaborators, and there will be opportunities to work together with excellent researchers within Sweden and abroad.

Project description
The project offers significant scientific freedom and flexibility around the central theme of rigorous foundations for robust and scalable learning. You may propose a relevant topic within the theme outlined above, or choose one of the following,

• Robust Learning under Distribution Shifts
• Optimization Theory for Large‑Scale Models
• Bayesian Inference and PAC‑Bayes Guarantees
• Don’t see your exact idea listed? We encourage bespoke proposals - outline your own research direction (max 2 pages) that aligns with our theme.

Our motivating applications arise in life sciences where we have extensive presence, and expansive collaborations spanning drug discovery, precision medicine and molecular biology. There are opportunities to work with real-world data presenting several challenges - noise, missingness, temporal variations, high-dimensionality, etc. There is also generous support for travel within the collaborative network, and to leading conferences.

Technical keywords for the position include computational learning theory, robust learning, large-scale optimization, Bayesian machine learning.

Duties
Research, publication, advising, and possibly teaching (max 20%). 

Requirements
PhD degree in, or a foreign degree equivalent to a PhD degree in Machine Learning/Scientific Computing/Mathematics/Statistics or a related field. The degree needs to be obtained by the time of the decision of employment. Priority will be given to applicants who have completed their degree no more than three years before the deadline for applications. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.

Excellent skills in spoken and written English are required. The candidate must clearly document a high degree of self-motivation in the application. Personal characteristics, such as a high level of creativity, thoroughness, and a structured approach to problem-solving are desired.

Additional qualifications
Demonstrated expertise in robust learning, Bayesian methods, and applications settings within life science is meriting. Publications at top machine learning conferences (ICLR, ICML, NeurIPS, etc.) are advantageous.

Application
The application must include:

1. a cover letter (max 2 pages), in English, briefly describing your motivation for applying for this position and the earliest possible employment date. The cover letter should include the heading ‘Suitability for this position’, containing a self-assessment on why you would be the right candidate for this position;
2. a CV;
3. transcript of PhD degree, including courses taken during PhD studies, or a draft of the thesis if the degree is not yet completed;
4. a list of publications;
5. a research statement describing your past and current research (max 1 page) and a proposal for future activities (max 2 pages)
6. references with contact information (names, emails and telephone number) and up to two letters of recommendation.

About the employment
The employment is a temporary position of 2 years according to central collective agreement. Full time position. Starting date 1 November, 2025 or as soon as possible, as agreed. Placement: Uppsala.

For further information about the position, please contact: Assistant Professor Prashant Singh, e-mail: prashant.singh@scilifelab.uu.se; Head of Division Elisabeth Larsson, elisabeth.larsson@it.uu.se.

Please submit your application by 15 September 2025, UFV-PA 2025/2322.

Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden https://uu.se/om-uu/jobba-hos-oss/