In my PhD studies, I develop a multiscale method for the efficient simulation of solutions to the wave equation in strongly heterogeneous media. This can be described by the acoustic wave equation with strongly oscillating coefficients in space and time. The aim is to combine multiscale strategies in space and time with a deep learning approach. The project is funded by the CRC 1173 Wave Phenomena, where it is listed under Project A15. My topics of interest are included in but not restricted to:

• Multiscale methods, espec. the Localized Orthogonal Decomposition (LOD) method
• Deep Learning and its applications to numerical methods
• Multifidelity methods for uncertainty quantification

Short CV:

• Since February 2025: PhD Student under the supervision of Roland Maier
• 2017-2024: Student of Mathematics at the University of Stuttgart, Thesis: Adaptive Bi-Fidelity Monte Carlo Estimation for Random Elliptic PDEs, Grade: very good with honors
• 2019-2022: Several student assistants at University of Stuttgart, Exchange student at the University of Toronto (Canada, 2022), Intern in Quantitative Risk Management at Mercedes Benz Bank (2021), Working Student in Simulation at ZF (2020)