Postdoctoral position in simulation-based inference for particle physics Full-time

Postdoctoral Position In Simulation-Based Inference For Particle Physics

Are you interested in working with machine learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, 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 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 numerical analysis, mathematical modelling, development and analysis of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of the Science for Life Laboratory (SciLifeLab) network, a national research infrastructure with a mandate to enable cutting-edge life science research in Sweden.

The successful candidate will join the Scientific Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together with the Theoretical Particle Physics group at the Department of Physics and Astronomy (Professor Stefano Moretti), which works on beyond-the-Standard-Model and dark-matter phenomenology and is a member of the CMS experiment at CERN. Together, the groups offer an international environment with a wide network of collaborators, generous support for conference travel, and access to national HPC resources (NAISS) and local GPU infrastructure.

This Postdoc position is part of the eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational methodological perspective and from the perspective of data-driven science applications. It is an arena where experts in computational science, data science and data engineering (systems and methodology) work closely together with researchers in (data-driven) sciences, industry and society to accelerate data-intensive scientific discovery. eSSENCE is a strategic collaborative research programme in e-science between three Swedish universities with a strong tradition of excellent e-science research: Uppsala University, Lund University and Umeå University.

In this project we envision a novel cross-faculty collaboration between the Division of Scientific Computing and the Department of Physics and Astronomy, where new methodology for simulation-based inference is developed and applied directly in realistic collider analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics.

Project description Searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider (LHC) compare high-dimensional collision data against detailed simulations whose likelihood cannot be evaluated, only sampled. Simulation-based inference (SBI) addresses this by training neural networks, such as flow-matching generative models, on simulated events. The project aims to develop efficient, robust and calibrated SBI methods for such searches that account for event selection and systematic uncertainties, and to demonstrate them on realistic searches at scale on national HPC resources. The project will primarily use Monte Carlo simulated data, but there is also the possibility of working with real open data from the ATLAS and/or CMS experiments. The methods are general and applicable well beyond particle physics.

Duties Research within the project described above, including method development, implementation, large-scale computational experiments on national HPC resources, and publication at machine learning and physics venues. The duties also include presenting results at international conferences, contributing to the group's open-source software, actively participating in the activities of the eSSENCE graduate school, and taking part in the supervision of students. A limited amount of teaching may be included (max 20%).

Requirements PhD degree in machine learning, scientific computing, statistics, physics or a related field, or a foreign degree equivalent to a PhD degree in machine learning, scientific computing, statistics, physics 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 experience in machine learning, in particular deep generative models and/or probabilistic modelling, 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 a high level of creativity, thoroughness, a structured approach to problem-solving, and the ability to work both independently and as part of an interdisciplinary team.

Additional qualifications Experience in simulation-based or likelihood-free inference, normalizing flows, flow matching or diffusion models is meriting. Experience in particle physics, in particular with collider simulation tools (e.g., MadGraph, Pythia, Delphes), statistical analysis of LHC data or beyond-the-Standard-Model phenomenology, is meriting. Experience with large-scale training on GPU and HPC systems, with design of experiments and active learning, with open-source software development, and with interdisciplinary collaboration is meriting. Publications at top machine learning conferences (ICLR, ICML, NeurIPS, AISTATS, etc.) or in leading physics journals are advantageous.

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 education. In 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 applicant's 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, prashant.singh@scilifelab.uu.se; Professor Stefano Moretti, stefano.moretti@physics.uu.se; Head of Division Elisabeth Larsson, elisabeth.larsson@it.uu.se.

Please submit your application by Thursday October 15, 2026, UFV-PA 2026/2734.