As a postdoctoral researcher in theoretical biophysics, my research focuses on understanding how biomolecules, particularly ion channels in the nervous system, move and function by applying and developing computational methods to simulate and analyze such systems. I'm particularly interested in the computation of free energy landscapes through physics-based simulation (molecular dynamics simulations, elastic network models) and machine learning/AI-based methods (particularly Markov state modeling), to predict how small molecules or genetic mutations can alter the function of a protein. Such calculations often require the use of large supercomputers and I therefore also think about how to make such calculations faster through high-performance computing, e.g. by contributing code to the molecular dynamics simulation software GROMACS.
My background is primarily in physics with a degree in engineering physics from KTH (2018), and Ph.D. in theoretical biophysics (June 2023). I have also worked as a support engineer at the PDC Center for High-Performance Computing and completed my master's studies in the research group of Nobel laureate Michael Levitt at Stanford University, USA.