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
- 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.
- Multifidelity data hierarchy study for excitation energies shows promising results for application of machine learning methodsV. Vinod and P. Zaspel. Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies. J. Chem. Theory Comput, 21, 6, 3077–3091, 2025. DOI: 10.1021/acs.jctc.4c01491; also available
- Open PhD position in structure-preserving scientific machine learning for port-Hamiltonian ODEs and DAEsAre you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking