Mission
The project-oriented research at the Weierstrass Institute is characterized by combining the mathematical disciplines of analysis, stochastics and numerics. This combination has great potential for solving complex applied problems such as the reliable extraction of information from large datasets or the suitable consideration of uncertainties in describing processes. In this way, the institute aids in solving current societal challenges.
Research
The institute dedicates itself to fundamental mathematical research as well as the development of algorithms and scientific software. During the problem-solving process, mathematical models of physical and technological systems are designed that properly capture observed phenomena, thereby providing access to highly developed mathematical analysis. At WIAS the phases of the solving process are repeated and coordinated until an optimal solution is found.
News
Marthe Vogt Prize awarded to Alexandra Quitmann
Mathematician will be honoured for her outstanding basic research at WIAS. Award ceremony will take place on 7 November 2024.
Events
Thursday, 07.11.2024, 10.00 (WIAS-405-406)
Software and Data Seminar
Dr. J. P. Thiele, WIAS:
Intermediate Unix Shell (command line interface)
Jobs
Research Assistant Position (f/m/d) (23/28)
Optimization with partial differential equations and variational inequalities
Research Assistant Position (f/m/d) (24/11)
Analysis, numerics and optimal control of partial differential equations, in particular models for anisotropic fluids
Research Assistant Position (f/m/d) (24/14)
Scalable, high-performance computational platform for digital twins of human organs
PhD student position (f/m/d) (24/20)
Researching principled optimal transport theory, optimization, and machine learning
PhD Student Position (f/m/d) (24/21)
Optimization with partial differential equations under uncertainty
Research Assistant Position (f/m/d) (24/22)
Optimization with partial differential equations and variational inequalities
Research Assistant Position (f/m/d) (24/25)
Bayesian methodology has been successfully applied to parameter estimation problems for PDEs, inverse problems, and stochastic processes.