The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences ( HGS MathComp ) at Heidelberg University is the leading graduate school in Germany that focuses on the complex topic of Scientific Computing. Located in a vibrant research environment, the school offers a uniquely structured interdisciplinary education for PhD students. HGS MathComp has
18 open PhD positions in the general research areas of Mathematics, Computer Science, Physics, Biology, Chemistry, and Life Sciences.
Scope and duration of the PhD positions:
- Three-year contract with the possibility of extension
- Salary according to TV-L E13 level; contract details depend on research project
- Faculty/department affiliation according to research group
- Start: October 1, 2023, unless otherwise stated below
- Application details: see bottom of page
PhD positions are available for the following PhD project topics:
Immersed interface methods for the simulation of cell movement
- Research group: Mathematical Methods of Simulation
- Supervisor: Prof. Dr. Guido Kanschat
- Start: Position available immediately
- Additional information: Joint project with Prof. Dr. Christine Selhuber-Unkel at IMSEAM; depending on focus, Dr. rer. nat. or Dr-Ing. possible
Multiscale Methods and Localized Model Reduction for Convection-Dominated Problems and Applications in Climate Modelling
- Research group: Numerical Analysis and Uncertainty Quantification
- Supervisor: Prof. Dr. Robert Scheichl
- Additional information: In collaboration with Prof. Dr. Jörn Behrens and Dr. Sanam Vardag.
Modeling and simulation of thermochemical energy conversion processes for the flexible use of hydrogen-based renewable fuels
- Research group: Multiphase Flows and Combustion
- Supervisor: Prof. Dr. Eva Gutheil
- Start: September 16 or October 1, 2023
Transformer-based cell tracking in virology
- Research group: Image Analysis and Learning
- Supervisor: Prof. Dr. Fred Hamprecht
- Start: Position available immediately
- Additional Information: The aim is to develop software that biologists will really be able to use. The project entails a large software component, and the ideal candidate brings experience in full stack software development, a deep understanding of machine learning, and a passion to help address some of humanity′s greatest challenges by working closely with virologists from SFB 1129. We are a small and friendly team that has previously made real contributions to the bioimage analysis community (ilastik, plantseg).
Geometric machine learning in quantum chemistry: learning kinetic energy density functionals
- Research group: Image Analysis and Learning
- Supervisor: Prof. Dr. Fred Hamprecht
- Start: Position available immediately
- Additional information: If successful, this project will enable a breakthrough in the computational cost of quantum chemical calculations. A solid understanding of quantum mechanics and machine learning are required. We are a small and friendly team and work on this exciting project together with the lab of Prof. Dr. Andreas Dreuw in the simplaix consortium.
Modelling cytoadhesion of malaria-infected red blood cells
- Research group: Schwarz group, theoretical biophysics
- Supervisor: Prof. Dr. Ulrich Schwarz
- Start: Position available immediately
- Additional information: A background in hydrodynamics and/or molecular dynamics would be helpful. This project is funded by SFB 1129 (integrative analysis of pathogen replication and spread).
Traction force microscopy for biological cells in three dimensions
- Research group: Schwarz group, theoretical biophysics
- Supervisor: Prof. Dr. Ulrich Schwarz
- Start: Position available immediately
- Additional information: A background in continuum mechanics and inverse problems would be helpful. This project is funded by the excellence cluster 3DMM2O.
Domain Uncertainty Quantification
- Research group: UQ and Scientific Machine Learning
- Supervisor: Jun. Prof. Dr. Jakob Zech
- Additional information: Applicants should possess a strong background in mathematics, ideally with a focus on PDEs. Experience with machine learning is also highly beneficial. Postdocs are welcome.
Machine Learning for Programming (2 PhD positions)
- Research group: Parallel and Distributed Systems (PVS)
- Supervisor: Prof. Dr. Artur Andrzejak
- Start: September 1 or October 1, 2023 for one position, later start possible for second position