open positions

Physics-Informed Machine Learning for Optical System Calibration

Finite Element Simulation of Multiphase Flow in Porous Media

Co-Supervisor(s): Martin Lesueur

Physics‑Informed LIF Inversion for Full‑Field Temperature and Scalar Transport in Convective Jet Flows

Co-Supervisor(s): Mike van Meerkerk

Adjoint-consistent deep learning for surrogate modeling of partial differential equations

Co-Supervisor(s): Romit Maulik (Purdue University)

Learning Preconditioners Using Neural Networks

Co-Supervisor(s): Rui Peng Li (LLNL)

Sparse identification of shape variation dynamics in cardiac anatomy, and generative modeling based on sparse regression

Co-Supervisor(s): Konstantinos Kevopoulos, Mathias Peirlinck

Conditional generative modeling of cardiac anatomy with flow-based architectures

Co-Supervisor(s): Konstantinos Kevopoulos, Mathias Peirlinck

Efficient Inexact Subdomain Solvers for Domain Decomposition Methods with RACE

Co-Supervisor(s): Filipe Cumaru (TU Delft), Dane Lacey, and Gerhard Wellein (FAU)

Several open projects

Co-Supervisor(s): Uwe Iben (Bosch)

Domain Decomposition-based Physics-Informed Neural Networks for Compressible Flows around Airfoils

Co-Supervisor(s): Simon Wassing (German Aerospace Center)

Predicting Patient Discharge Dates Using Machine Learning

Co-Supervisor(s): Cindy Pistorius (TU Delft, Optimization)

Graph Neural Network Surrogates to Replace Agent-Based Models for Pandemic Response

Co-Supervisor(s): Martin Kühn (German Aerospace Center & University of Bonn)

Improving Nonlinear Solver Convergence Using Machine Learning

Co-Supervisor(s): Tycho van Noorden (COMSOL)

Physics-Informed Machine Learning for Optical System Calibration

Reusability of preconditioners

Co-Supervisor(s): Victorita Dolean (TU Eindhoven)