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
- 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
- New development in multi-fidelity machine learning methods opens up possibilities for the use of heterogeneous data for the prediction of quantum chemical propertiesVinod, V., & Zaspel, P. (2024). Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties. arXiv preprint 2407.17087, http://arxiv.org/abs/2407.17087 Multi-fidelity methods in machine learning (ML)
- Dataset of diverse quantum chemical properties to enable research and benchmarking of multifidelity machine learning models released!Vinod, V., & Zaspel, P. (2024). CheMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules. arXiv preprint arXiv:2406.14149 https://doi.org/10.48550/arXiv.2406.14149. Vinod, V., &