Associate Prof. Dr.techn. Dipl.-Ing.
- Partial Differential Equations, Stochastic Differential Equations, Stochastic finite element methods, Multi-body problems, Machine Learning
Reinforcement learning; Bayesian methods for ordinary- and partial-differential-equation models; large language models.
- Bachelor Thesis in Computer Science / 194.112 / PR
- CAIML seminar / 192.138 / SE
- Generative AI / 194.154 / VU
- Introduction to Machine Learning / 194.025 / VU
- Project in Computer Science 1 / 194.145 / PR
- Seminar for PhD Students / 194.110 / SE
Reliable reinforcement learning for sustainable enery systems
2023 – 2025 / Austrian Research Promotion Agency (FFG)
Reinforcement learning for games with imperfect information – Teaching an agent the game of Schnapsen
Salzer, T. (2023). Reinforcement learning for games with imperfect information – Teaching an agent the game of Schnapsen [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.86961
Download: PDF (1.2 MB)
PDE Models for Nanotechnology
2013 / START Prize / Austria / Website
2013 / START-Programm / Austria
Erwin Schrödinger Fellowship (FWF)
2003 / Schrödinger-Stipendium / Austria
Soon, this page will include additional information such as reference projects, activities as journal reviewer and editor, memberships in councils and committees, and other research activities.
Until then, please visit Clemens Heitzinger’s research profile in TISS .