Several Interdisciplinary Phd Positions - Heidelberg, Deutschland - Universität Heidelberg

Universität Heidelberg

Geprüftes Unternehmen

Heidelberg, Deutschland

vor 2 Wochen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

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

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
Additional information: Research fields are ML/AIbased approaches for codetocode translation, vulnerability detection, performance optimization, and AIassisted programming. Background and experience in deep learning for NLP and/or source code analysis are a plus. Postdocs are welcome. Very good command of the German language is a requirement for at least one of these positions.

Model based AI:
Physics Informed Neural Networks (fundamental research)

Research group: Data Analysis and Modeling in Medicine
Supervisor: Prof. Dr. Jürgen Hesser
Additional information: Background in mathematics with experience in machine learning and PDEs or computer science with good background in machine learning (ideally physics informed neural networks) but also with a good math background

Machine Learning and Model-based controller (applied research)