© Uni MS

We are Mathematics Münster

We aim to further develop mathematics in Münster into a research centre with high international visibility. We will tackle fundamentally important mathematical problems, viewing mathematics as an organic whole with countless interactions.

Mathematics Münster provides an umbrella integrating the four mathematical institutes of the University of Münster (Mathematical Institute, Institute for Analysis and Numerics, Institute for Mathematical Stochastics and the Institute for Mathematical Logic and Foundations of Mathematics) in order to foster collaboration between the 16 mathematical fields and scientific exchange.

News about members and guests at Mathematics Münster

© SLMath

Mini-course "Stochastic PDEs and Scaling Limits" available now

Talk series by Hendrik Weber at SLMath

Prof. Dr. Hendrik Weber, investigator at Mathematics Münster, is currently spending a research stay at the Simons Laufer Mathematical Sciences Institute (SLMath) in Berkeley, USA, funded by a Clay Senior Scholar Award. As part of the introductory workshop “Kinetic Theory & Stochastic Partial Differential Equations”, he gave a mini-course on “Stochastic PDEs and Scaling Limits”. The video recordings are now available.

© MM/vl

"Coming to Münster was one of the most significant turning points in my academic career"

Interview with Shirly Geffen

Prof. Dr. Shirly Geffen, investigator of Mathematics Münster, works in the field of operator algebras, and more specifically, C*-algebras. Since 2024, she has held a permanent professorship at the University of Münster. In this interview, she shares how she discovered her research field, how her academic path led her from Israel to a MATHRIX assistant professorship in Münster, and what she finds particularly beautiful about mathematics. 

© MM/vl

New WiRe Fellow: Amra Hasečić

We are delighted to welcome Dr. Amra Hasečić as a WiRe Fellow at Mathematics Münster. Originally from Bosnia and Herzegovina, she has joined the group led by Mario Ohlberger as a postdoctoral researcher. Her research focuses on investigating the potential of machine learning to predict internal temperature fields in fluid flows, utilising data from simplified simulations that do not account for radiation.