We are recruiting two Postdoctoral Researchers to join the Scientific Machine Learning group at the Department of Information Technology, Science for Life Laboratory (SciLifeLab), Uppsala University, Sweden. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification in large pre-trained models, generative models, simulation-based inference, and robust and active learning.
(i) Simulation-based inference for particle physics. Develop efficient, robust and calibrated simulation-based inference methods (flow-matching generative models) for searches for dark matter and other “invisible” new-physics signals at the LHC, in a new cross-faculty collaboration with the Theoretical Particle Physics group of Prof. Stefano Moretti (CMS Collaboration). Part of the eSSENCE graduate school in data-intensive science. Full details and application.
(ii) Probabilistic methods for foundation and world models. Make foundation models and world models uncertainty-aware, calibrated, robust and useful for scientific decision-making. The topic is open within this theme, with motivating applications in the life sciences; we welcome your own proposal (max 2 pages). Full details and application.
Both roles offer scientific freedom, an interdisciplinary and international environment, access to national GPU resources (NAISS) and local infrastructure, and support for conference travel. The positions are fully funded for two years, and we take pride in our commitment to sustainable employeeship – offering safe, favourable working conditions with attractive pay and benefits. Read more about our benefits and what it’s like to work at Uppsala University.
Applicants should hold a PhD (or equivalent) in machine learning, scientific computing, statistics, physics, mathematics or a related field by the time of joining, with documented experience in modern deep learning; publications at leading ML venues are a merit.
Application deadline: 15 October 2026. Start date: 1 November 2026 (or by agreement).
Contact Associate Professor Prashant Singh with any questions (for position (i), also Prof. Stefano Moretti).