Postdoctoral in probabilistic methods for foundation and world models

Do you want to work with probabilistic machine learning for the next generation of AI models in an international environment with competent and nice colleagues? Welcome to apply for a postdoctoral position at Uppsala University.

This is an abbreviated version of the ad. The full advertisement is available on Uppsala University's website, uu.se/jobb.

The Department of Information Technology is Uppsala University's third largest institution with more than 350 employees and participates in Wallenberg AI, Autonomous Systems and Software Program (WASP). More information is available on the department's website.

The position is located in the Department of Computational Science (TDB), one of the world's largest research environments in computational science and an important part of e-science collaboration eSSENCE and Science for Life Laboratory (SciLifeLab), a national research infrastructure for life sciences.

You will be part of the research group Scientific Machine Learning at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, focusing on uncertainty quantification in large trained models, generative models, simulation-based inference, and robust and active learning.

Project descriptionThe assignment offers great scientific freedom within the theme of probabilistic methods for foundation models and world models: making them uncertain, calibrated, robust and useful for scientific decision-making. You can start from one of the following or suggest your own subject in the theme (describe your research focus, max 2 pages): Unsafety quantification, calibration and reliability in large trained models. Probabilistic generative models and world models. Probabilistic machine learning for scientific detection.

Motivating applications are found in life sciences, where the group cooperates via SciLifeLab in areas such as microscopy, drug development and precision medicine, with access to real, large-scale and multimodal data. The focus is on basic high-quality AI/ML methods, which can be used for applications.

TasksResearch, publication and conference presentations, contributions to the group's open software and participation in student supervision. A limited percentage of teaching can be included (maximum 20 %).

Qualification requirementsDoctor's degree in machine learning, computer science, computational science, mathematics, statistics or a related field, or a foreign degree that is considered equivalent to a doctoral degree in one of these areas. The degree must be completed no later than the time when the employment decision is taken. In particular, the person who has completed a degree not exceeding three years ago should be considered. When calculating the three-year framework period, the starting point is the closing date for applications. If there are special reasons, such a degree may have been completed earlier. Special reasons include leave due to illness, parental leave, positions of trust in trade unions, etc.

Documented research experience of modern deep learning and very good programming skills in Python and a modern deep learning framework (e.g. PyTorch or JAX) are required. Good knowledge of English in speech and writing is required. The candidate shall clearly document a high degree of self-motivation in the application. Great emphasis is placed on personal qualities such as creativity, accuracy, a structured way of working and the ability to work independently and in teams.

Wishful/crediting otherwisePublications at leading machine learning or computer vision conferences (NeurIPS, ICML, ICLR, CVPR, etc.) are highly meriting. Expertise in Bayesian methods, generative models, multimodal models, world models or simulation-based inferences are meritorious, as well as experience in large-scale training at GPU clusters, open software development and applications in life sciences.

Teaching experience is meriting but not a requirement. Teaching experience may include, for example, teaching, supervision, mentoring, work as an aid teacher, in-service training or other educational activities, within or outside higher education. Special emphasis is placed on activities that support students' learning in computer science, information technology or related subjects.

The application must contain: A curriculum vitae (CV), A copy of relevant grade documents (translated into Swedish or English), A publication list, Up to five selected publications in electronic format, A research description that describes your previous and current research (max 1 page) and a proposal for future activities (max 1 page), Contact information for two references,

If the employment is limited in time for two years according to a central collective agreement. The scope is full-time. Admission 1 November 2026 or by agreement. Location: Uppsala Information about the position is provided by: Senior Lecturer Prashant Singh, prashant.singh@scilifelab.uu.se; Head of Department Elisabeth Larsson, elisabeth.larsson@it.uu.se.

In this recruitment we have replaced the personal letter with questions that you answer in connection with your application. The responses will be used as part of the selection process.

Welcome with your application no later than Thursday 15 October 2026, UFV-PA 2026/2764 Uppsala University is a broad research university with a strong international standing. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all 7 500 employees and 53 000 students who with curiosity and commitment make Uppsala University one of the country's most exciting workplaces.

Read more about our benefits and what it's like to work at Uppsala University https://uu.se/om-uu/jobba-hos-oss/ The employment may be safety tested. In the case of security clearance, a condition for employment is that applicants are approved.

We reject offers of recruitment and advertising assistance.

The application is received in Uppsala University's recruitment system.

Trade union representatives: Saco-S - saco-s@uu.se, Seko - seko@uadm.uu.se, ST (OFR/S) - ofr@uu.se