In our team at University of Wuppertal, we tackle challenges in machine learning, uncertainty quantification and high performance computing. We perform research at the overlap of computer science and applied mathematics with special emphasis on methods and applications targeted towards engineering, natural science, medicine and beyond.
Feel free to explore our recent research and teaching in this field!
We are continuously looking for talented PhD candidates and Postdocs. Please apply! In particular, we have this open job offer.
Recent news
- Pioneering Research on Excitation Energy Transfer in Light-Harvesting Systems Published in Advanced Theory and SimulationsD. Lyu, V. Vinod, M. Holzenkamp, Y. M. Holtkamp, S. Maity, C. R. Salazar, U. Kleinekathöfer, P. Zaspel. Excitation Energy Transfer between Porphyrin Dyes
- Benchmarking Data Efficiency in Advanced Machine Learning Models for Quantum ChemistryV. Vinod, P. Zaspel; Benchmarking data efficiency in Δ-ML and multifidelity models for quantum chemistry. J. Chem. Phys. 163 (2): 024134, 2025. DOI: 10.1063/5.0272457;
- Multifidelity methods predict energies of organic molecules with coupled cluster accuracyV. Vinod, D. Lyu, M. Ruth, U. Kleinekathöfer, P. R. Schreiner, and P. Zaspel. Predicting molecular energies of small organic molecules with multifidelity methods. J. Comput.