The rector, the first prize-winner and two students stand by the newly inaugurated Prof. Dr. Dominik Grimm (2nd from left) stands next to the tree for excellent teaching on the green space behind the “Sustainable Chemistry” teaching and research building together with student representatives Adrian Heider (l.) and Julia Bronold and TUM Campus Rector Prof. Dr. Volker Sieber. Photo: Zellmer/TUMCS

Recently, the student council of the TUM Campus Straubing (TUMCS) for Biotechnology and Sustainability awarded the prize for excellent teaching for the first time to Prof. Dr. Dominik Grimm. A “Tree of Teaching” behind the new “Sustainable Chemistry” teaching and research building facing the Danube is dedicated to him. The student council had already chosen Prof. Grimm as the winner last year, and now the students congratulated him with a sign next to a newly planted silver willow after completion of the new building and the adjacent green space.

Prof. Dr. Volker Sieber, Rector of the TUM Campus Straubing, thanked Prof. Grimm for his outstanding teaching and was pleased about the initiative of the students. Each year, the students vote for the best lecturer in a survey, which is then announced by the student council – which represents the interests of all students. The vote for the award winner for 2021 will take place shortly.

Prof. Dr. Dominik Grimm, who comes from Plattling (Deggendorf district), has been head of the Bioinformatics professorship at Weihenstephan-Triesdorf University of Applied Sciences (HSWT) at the TUM Straubing campus since 2018. The HSWT has been successfully cooperating with the Technical University of Munich for several years – first at the then Straubing Science Center, and since 2017 at the newly founded TUM Campus Straubing for Biotechnology and Sustainability.

Bioinformatics is a young research field that develops computational and statistical tools for the analysis, storage and visualization of biological and biomedical data. One of Prof. Grimm’s main research areas is to develop novel computational tools and machine learning methods to gain a deeper understanding of the underlying architecture of complex processes for the bioeconomy.