PhD position in multilevel interacting particle methods for Bayesian inversion

Candidates must hold a master degree in Mathematical Engineering or (Applied) Mathematics (or equivalent).

Candidates should have a solid background in numerical methods for differential equations, simulation of stochastic processes and/or optimization. 

Candidates should have experience with programming of scientific software.

Excellent proficiency in English is required, as well as good communication skills, both oral and written.

To reliably use simulation-generated predictions in science and engineering, one needs trustworthy mathematical models that are calibrated to measurement data. We are motivated by applications in engineering in which the system models are partial differential equations (PDEs) with potentially infinite-dimensional (e.g., space-dependent) parameters and state variables. Inferring these parameters and/or states from large amounts of possibly high-resolution data leads to computationally intensive inverse problems. The team aims ...