In our team, 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 currently have this running job advertisement.
Recent news
- 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., &
- Optimal Combination with Multifidelity Machine Learning Achieves Coupled Cluster AccuracyVinod, V., Kleinekathöfer, U., & Zaspel, P. (2024). Optimized multifidelity machine learning for quantum chemistry. Machine Learning: Science and Technology, 5(1), 015054 http://doi.org/10.1088/2632-2153/ad2cef. Recent