PhD Position in Computational Physics, Machine Learning, and Experimental Reactor Design for Magnetic Particle Transport Technical University of Munich in Germany

This PhD position at the Technical University of Munich (TUM) offers a unique opportunity to work at the intersection of computational physics, machine learning, and experimental reactor design. The research project focuses on developing advanced physics-informed neural network (PINN) simulations for magnetic particle transport in fluid flows, with direct applications in resource extraction and separation technologies.

The project is structured around two main objectives. First, you will design and implement a simulation framework using PINNs to model the dynamics of magnetic particles under coupled hydrodynamic and magnetic forces. This involves particle tracing in complex flow fields, integrating magnetic field gradients and magnetophoretic forces, and exploring PINNs for both forward and inverse modeling problems. Second, you will translate simulation insights into the design and realization of a laboratory-scale experimental reactor. This hands-on component includes building the reactor, investigating particle retention and transport, and validating model predictions against experimental data.

Applicants should have a strong background in physics or engineering, with a Master’s degree in Physics, Electrical Engineering, or a related discipline. Essential skills include a solid understanding of fluid dynamics and/or electromagnetism, programming experience (preferably Python), and a keen interest in machine learning and scientific computing. Desirable qualifications include experience with numerical simulation tools (such as COMSOL or CFD frameworks), knowledge of scientific machine learning (especially PINNs), familiarity with particle-based simulations or Monte Carlo methods, and hands-on experimental work. Knowledge of the German language is advantageous but not mandatory.

The position is open to disabled persons, who will be given preference in case of equivalent suitability, aptitude, and professional performance. The application process requires submission of a motivational statement, CV, and copies of degrees and transcripts to PD Dr. habil. Bernhard Gleich ([email protected]) with Matthis Bünning ([email protected]) in CC. Selected candidates will be invited to meet the team in Garching. Please review the data protection information provided by TUM before applying.

For more details and to apply, visit the official application portal: TUM Job Portal.