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!
Recent news
- Chemical space sampling with novel active learning cuts training data cost by an order of magnitudeVivin Vinod and Peter Zaspel have developed a faster, more efficient way to train machine learning models on complex chemical data. Detailed in their
- 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;