Postdoc in simulation-based inference for particle physics

Do you want to work with machine learning and simulation-based inference in the search for dark matter (or other "invisible" signals of new physics) at the Large Hadron Collider, in an international environment with competent and pleasant colleagues? Welcome to apply for a postdoctoral position at Uppsala University. This is a shortened version of the advertisement. The full advertisement can be found on Uppsala University's website, uu.se/jobb. The Department of Information Technology is Uppsala University's third largest department with over 350 employees. The position is located at the Department of Computational Science (TDB), one of the world's largest research environments in computational science with extensive activities in, among others, machine learning, optimization, and high-performance computing, and an important part of eSSENCE and SciLifeLab. The person employed will be part of the Scientific Machine Learning research group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models, and robust machine learning, in close collaboration with the theoretical particle physics group at the Department of Physics and Astronomy (Professor Stefano Moretti), which researches phenomenology beyond the Standard Model and dark matter and is a member of the CMS experiment at CERN. The project establishes a new faculty-crossing collaboration where new methodology for simulation-based inference is developed and applied directly in realistic analyses of collider data. The postdoc will be jointly supervised by both groups and will receive support for conference travel as well as access to national HPC resources (NAISS) and local GPU infrastructure. The position is part of the eSSENCE doctoral school in data-intensive science, an arena where experts in computational science, computer science, and computer engineering work closely with researchers in data-driven sciences, industry, and society. eSSENCE is a strategic research collaboration in e-science between Uppsala University, Lund University, and Umeå University.

Project Description The search for dark matter at the LHC involves comparing high-dimensional collision data with detailed simulations whose likelihood cannot be calculated, only sampled. Simulation-based inference (SBI) approaches this by training neural networks, such as generative models based on flow matching, on simulated events. The project aims to develop efficient, robust, and calibrated SBI methods that take into account event selection and systematic uncertainties, and to demonstrate them in realistic large-scale searches. The project is primarily based on simulated data, but there is also the possibility to work with open data from ATLAS and/or CMS. The methods are general and applicable far beyond particle physics.

Tasks Research within the project, including method development, implementation, large-scale computational experiments, and publication, as well as presentation at international conferences, contribution to the group's open software, participation in eSSENCE doctoral school activities, and involvement in supervising students. A limited portion of teaching may be included (at most 20 %).

Qualification Requirements A doctoral degree in machine learning, computational science, statistics, physics, or a related field, or a foreign degree assessed to correspond to a doctoral degree in these fields. The degree must be completed no later than when the employment decision is made. Primarily, candidates who have completed their degree at most three years ago should apply. When calculating the three-year timeframe, the starting point is the application deadline. If there are special reasons, such a degree may have been obtained earlier. Special reasons refer to leave due to illness, parental leave, positions of trust within trade unions, etc. Documented experience in machine learning, especially deep generative models and/or probabilistic modeling, as well as very good programming skills in Python and a modern deep learning framework (e.g., PyTorch or JAX) are required. Good proficiency in English, both spoken and written, is required. The candidate must clearly document a high degree of self-motivation in the application. Great importance is placed on personal qualities such as creativity, precision, a structured way of working, as well as the ability to work both independently and in an interdisciplinary team.

Other Desirable/Meritorious Qualifications Experience with simulation-based inference, normalizing flows, flow matching, or diffusion models; of particle physics (e.g., MadGraph, Pythia, Delphes, or analysis of LHC data); of large-scale training on GPU/HPC systems, active learning, and open software development is meritorious, as are publications at leading machine learning conferences or physics journals. Teaching experience (e.g., teaching, supervision, mentorship, or other pedagogical activities) is meritorious but not a requirement; particular weight is placed on activities that support student learning in computer science, information technology, or related subjects.

Application The application shall include:

• CV;
• copy of relevant transcripts (in Swedish or English);
• publication list; up to five selected publications in electronic format;
• a research description of previous and current research (max 1 page) as well as a proposal for future activities (max 1 page);
• contact details for two references.

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

About the Employment The employment is fixed-term for two years according to the central collective agreement. The scope is full-time. Commencement: 1 November 2026 or by agreement. Location: Uppsala. Information about the employment is provided by: Associate Professor Prashant Singh, prashant.singh@scilifelab.uu.se; Professor Stefano Moretti, stefano.moretti@physics.uu.se; Head of Department Elisabeth Larsson, elisabeth.larsson@it.uu.se. Welcome with your application no later than Thursday, 15 October 2026, UFV-PA 2026/2734.