Jan Kierfeld

Prof. Dr. Jan Kierfeld is a faculty member in the Department of Physics at Technical University of Dortmund, where he leads a research group focused on soft matter theory and biological physics. His work bridges statistical physics, mechanics, and hydrodynamics of soft and biological systems, with strong interdisciplinary connections to materials science and biophysics.

His research interests include polymer physics, cytoskeletal filaments (actin and microtubules), elastic capsules and shells, active matter, and the development of novel simulation techniques such as event-chain Monte Carlo. He is particularly interested in how chemical energy (e.g., ATP/GTP hydrolysis) drives mechanical forces in biological systems, and in the mechanics of semiflexible polymer networks and microswimmers. His group also pioneers the application of machine learning to problems in soft matter, such as pendant drop tensiometry and traction force microscopy.

The recent publications of Prof. Kierfeld span topics in biophysics, soft matter, and computational physics, showing a strong trend toward integrating theoretical modeling with experimental collaboration, especially in microswimmer dynamics, microtubule mechanics, and interfacial phenomena. His work frequently appears in journals such as Soft Matter, Physical Review, Biophysical Journal, and Nature Communications.

He has no listed scientific awards in the provided text, but his active publication record and leadership in DFG programs (e.g., SPP1726 Microswimmers) indicate significant recognition in the field. He advises students and postdoctoral researchers in theoretical and computational soft matter physics, though specific names are not listed. His research is supported by grants from German funding agencies such as the DFG.

Prof. Kierfeld’s group develops and applies advanced simulation methods and collaborates with experimentalists on problems involving elastic instabilities (buckling, wrinkling), microswimmers, and chemomechanical models of cellular structures. The group maintains strong technical development in numerical algorithms and data analysis tools, including open-source software like MLFTM for traction force microscopy.