Postdoc in interacting particle methods for Bayesian inversion with model error

Candidates must hold a PhD 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.

Specific experience with sampling methods for Bayesian is highly appreciated. Experience with cardiac electrophysiological modeling is a plus.

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 paramet...