Postdoctoral Fellow for machine learning enhanced multiscale reactive fluid dynamics
- Conduct research in the area of cross-scale modelling of reactive multiphase flow (boiling) in complex heterogeneous materials, including conjugate heat transfer and surface reactivity (dissolution / precipitation) with application to nuclear reactor flows and CRUD formation
- Use machine learning techniques to accelerate the numerical solvers and numerical analysis, the existing framework of the in-house scientific multiscale codes and machine learning tools will be the basis for further developments
- 2D/3D multiphase numerical simulations using the CPU and GPU capabilities of the in-house lattice-Boltzmann codes, as well as commerical software
- Co-development of codes, including benchmarking and validation
- Publication of results in journals and conferences
- PhD degree in Mechanical or Nuclear Engineering / Natural Sciences / Environmental Sciences
- Experience in numerical simulations, fluid dynamics and multiphase flows (ideally with lattice Boltzmann method)
- Strong experience in numerical modelling and fluid dynamics, as well as programming experience (C / Cuda / Python)
- Very good command of English is required (spoken and written)
Villigen / Argovie / Aargau / CH-5234 Villigen / Würenlingen