PhD student (m/f/x) on the subject of probabilistic turbulence models
Core
Constructing adaptive, probabilistic turbulence models using entropy-stable simulations, model reduction, stochastic modelling, and data assimilation for applications like wind-turbine wakes and CO2 transport.
Role type
PhD researcher (Scientific Computing)
Builds
Entropy-stable fluid flow simulations and probabilistic turbulence models
Domain
Scientific Computing / Computational Fluid Dynamics / Applied Mathematics
Deliverable
production ML models | research
Required skills
numerical analysis, stochastic methods, generative machine learning, computational fluid dynamics, data assimilation, Python, Julia
Preferred skills
strong mathematical background, fluid flow simulation interest, academic writing, presentation skills
Technologies
Python, Julia
Responsibilities
Construct adaptive probabilistic turbulence models; focus on stochastic fundamentals, generative machine learning, or data assimilation based on background; collaborate with SYMBIOSIS team members.
Seniority
PhD student, research execution