Jan Dufek

Jan Dufek is an Associate Professor in the Department of Nuclear Science & Engineering at KTH Royal Institute of Technology. His research focuses on advancing numerical methods for Monte Carlo neutron transport simulations, with applications in nuclear reactor design and safety. Key areas include coupled simulations of thermal-hydraulic feedback, Monte Carlo burnup methods, fission source convergence acceleration, and fission matrix-based techniques. He also develops deterministic nodal nuclear data models using polynomial regression to handle multi-dimensional state variables efficiently.

He teaches courses such as Monte Carlo Methods and Simulations in Nuclear Technology and Nuclear Reactor Physics, serving as an examiner and course responsible. His work contributes to projects like the McSAFE initiative, aiming to enhance high-performance Monte Carlo methods for reactor safety. Collaborations include studies on deep learning for nuclear fuel composition prediction and transient analysis using hybrid stochastic-deterministic approaches. His research bridges computational efficiency with practical reactor engineering challenges.

Dr. Dufek is affiliated with the NRT (Nuclear Reactor Technology) group at KTH, fostering interdisciplinary advancements in reactor physics and computational methodologies.