Due to COVID-19 restrictions, the Institute is currently only accessible to visitors by prior appointment. A large number of scientific events are meanwhile taking place online, to which also interested parties from outside are very welcome. »
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.
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.
Grief for Konrad Gröger
WIAS mourns the loss of its honorary member and former colleague, Prof. Konrad Gröger, who passed away on September 14, 2020 after a long and serious illness.
TetGen 1.6 Release
New version of successful mesh generator available
Wednesday, 21.10.2020, 11.30 (WIAS-405-406)
Seminar Interacting Random Systems
Variational structures beyond gradient flows: a macroscopic-fluctuation-theory perspective joint work with Rob Patterson (WIAS) and Upanshu Sharma (FU Berlin)
PhD Student (f/m/d) (20/17)
Theoretical Physics, Maxwell equations, modeling of optical systems, nonlinear dynamics
Project Assistant (f/m/d) (20/18)
Qualified administrative assistant
PhD student position (m/f/d) (20/19)
mathematical image processing, calculus of variations, optimization with partial differential equations, numerical solution of partial differential equations
Research Assistant Position (m/f/d) (20/20)
Stochastic Algorithms and Nonparametric Statistics, Analysis of brain signals by Bayesian Optimal Transport, Machine learning