Postdoc position in probabilistic methods for foundation and world models

Are you interested in working with probabilistic machine learning for the next generation of AI models uncertainty-aware foundation models generative models and world models 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 favourable working conditions We welcome you to apply for a postdoctoral position at Uppsala University.

The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala Universitys third largest department have around 350 employees including 120 teachers and 120 PhD students. Approximately 5000 undergraduate students take one or more courses at the department each year. The department also participates in the Wallenberg AI Autonomous Systems and Software Program (WASP). 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) one of the worlds largest research environments in computational science with large activities in areas such as machine learning optimization scientific software development and high-performance computing. The division is an important part of the eSSENCE e-science collaboration and of the Science for Life Laboratory (SciLifeLab) network a national research infrastructure for life sciences.

The successful candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory methods and software for data-driven science with a current focus on uncertainty quantification in large pre-trained models (vision-language models) generative models (flow matching diffusion) simulation-based inference and robust and active learning. The group has a wide network of collaborators and strong access to compute through national GPU systems (NAISS e.g. Berzelius and Arrhenius) and local GPU infrastructure.

Project description
The position offers significant scientific freedom around the central theme of probabilistic methods for foundation models and world models: making them uncertainty-aware calibrated robust and useful for scientific decision-making by tackling the challenging open problems that matter most for how such models are used today. You may propose your own topic within the theme or start from one of the following directions:

• Uncertainty quantification calibration and reliability of large pre-trained models
• Probabilistic generative models and world models
• Probabilistic machine learning for scientific discovery
• Dont see your exact idea listed We encourage bespoke proposals outline your own research direction (max 2 pages) within the theme above.

Duties
Research publication and presentation of results at international conferences contributions to the groups open-source software and participation in the supervision of students. A limited amount of teaching may be included (max 20%).

Requirements
PhD degree in machine learning computer science scientific computing mathematics statistics or a related field or a foreign degree equivalent to a PhD degree in machine learning computer science 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. 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.

Documented research experience in modern deep learning (e.g. generative models Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning framework (e.g. PyTorch or JAX) are required. Excellent skills in spoken and written English are required. The candidate must clearly document a high degree of self-motivation in the application. Great emphasis will be placed on personal characteristics such as creativity thoroughness a structured approach to problem-solving and the ability to work both independently and in a team.

Additional qualifications
Publications at top machine learning or computer vision conferences (NeurIPS ICML ICLR CVPR AISTATS etc.) are highly meriting. Expertise in Bayesian methods generative models multimodal models world models or simulation-based inference is meriting as is experience with large-scale training on GPU clusters open-source software development and applications in the life sciences.

Teaching experience is considered a merit. This may include teaching supervision mentoring course assistance providing internal training or other educational activities within or outside higher assessing such experience particular consideration will be given to activities that support students learning in computer science information technology or closely related subjects. The assessment will take the applicants career stage into account and extensive teaching experience is not expected.

Application
The application must contain:

• A curriculum vitae (CV)
• A copy of relevant grade documents (translated into Swedish or English)
• A list of publications Up to five selected publications in electronic format
• A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page).
• Contact information for two references.

About the employment
The employment is a temporary position of two years according to central collective agreement. Full time position. Starting date 1 November 2026 or as agreed. Placement: Uppsala

For further information about the position please contact: Associate Professor Prashant Singh ; Head of Division Elisabeth Larsson

In this recruitment we have replaced the cover letter with questions that you are asked to answer when making your application. The answers will be used as a part of the selection process.

Please submit your application by Thursday October 15 2026 UFV-PA 2026/2764.

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position may be subject to security vetting. If security vetting is conducted the applicant must pass the vetting process to be eligible for employment.

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