2 PhD positions with a focus on Accelerating Rarefied Gas Dynamics

Eindhoven University of Technology is looking for two motivated PhD candidates that, combining model-based (physics) and data-driven (machine-learning) approaches, will develop innovative, highly accurate and highly efficient solvers for rarefied gas flows.

Computational fluid dynamics is an essential enabler for science and for many outstanding societal challenges. Many key advanced and emerging technologies require unprecedented control of heat and mass transfer in flows, from continuum to highly-rarefied conditions, often in presence of electromagnetic fields, chemical reactions, and complex interactions with boundaries. Due to the non-equilibrium nature of rarefied flows and the pronounced influence of molecular effects, these transport processes are highly complex and occur in non-standard circumstances.