SFB/Transregio 388 investigates the interplay between rough analysis and stochastic dynamics. Central aspects include rough paths and subsequent developments for nonlinear stochastic partial differential equations. The theory of signatures and rough volatility also provides important connections to algebra, statistics, and financial mathematics.
Website: https://sites.google.com/view/trr388/ 

The group “Numerical analysis and stochastic” at Freie Universität Berlin (https://www.mi.fu-berlin.de/en/math/groups/ag-num-ana-and-stoch/index.html) focuses on analysis and numerical analysis of (stochastic) partial differential equations (PDEs), especially on interacting particle systems, surface PDEs and uncertainty quantification. The group "Numerical analysis of stochastic and deterministic partial differential equations" at Freie Universität Berlin (https://www.mi.fu-berlin.de/math/groups/naspde) focuses on applied and computational mathematics, in particular optimization, inverse problems and uncertainty quantification.

Job description:
The aim of the project B06 of the SFB/TRR 388 is to develop an abstract well-posedness and regularity theory for (S)PDEs on random time-dependent domains and its numerical analysis. We will consider quasi-Monte Carlo methods (QMC) for numerical discretization of quantities of interest in the forward as well as in the (Bayesian) inverse setting. Furthermore, we will analyze well-posedness of SPDEs on time-dependent domains and study SPDEs on random timedependent domains. The third-party funded research project provides an opportunity to do a doctorate.

Requirements:
A completed scientific university degree (Master’s degree) in mathematics until October 2024

Desirable:
- Very good university degree in mathematics
- Profound knowledge of stochastic analysis, particularly in SPDEs
- Strong knowledge of numerical methods for partial differential equations
- Programming skills in Matlab or Python
- Profound understanding of Uncertainty Quantification, especially in Bayesian inverse problems and Monte Carlo methods
- Excellent English language skills and good scientific writing and presentation skills

For further information, please contact Prof. Dr. Ana Djurdjevac (adjurdjevac@zedat.fu-berlin.de / +43 30 838 60608).