Prof. Dr. Markus Götz
Academic Career and Research Areas
My research focus is on scalable machine learning and data analytics for high-performance computing environments. In particular, I am interested in distributed and parallel training of neural networks, resource-efficient and low-rank model architectures, energy-aware AI, and scalable classical machine learning algorithms for scientific data. My work spans applications in renewable energy systems, weather modeling, and biomolecular simulation. I care deeply about sustainable and reproducible research software.
B. Sc IT-Systems Engineering, HPI, University of Potsdam (2007–2010); intern at CERN, Switzerland (2011); Erasmus at Blekinge Tekniska Högskola, Sweden (2010–2011); M.Sc. IT-Systems Engineering, Potsdam (2010–2014); PhD in Computational Engineering, University of Iceland/Forschungszentrum Jülich (2014–2017); Postdoctoral Researcher, KIT (2018–2019); Group Leader, Helmholtz AI Energy, KIT (2020–present); and, starting 09/2026, Assistant Professor for Scalable and Efficient Machine Learning, TUM Campus Heilbronn.
Awards
- ACM Gordon Bell Award in Climate Modelling Finalist, Supercomputing (SC), 2026
- Best Paper Award, Platform for Advanced Scientific Computing (PASC), 2026
- EU-SOLARIS Award, 2025
- Best Poster Award, ISC High Performance, 2025
- Best Paper Award, International Conference on Neural Information Processing (ICONIP), 2024
Key Publications (all publications)
Kieckhefen, D., Götz, M., Heyen, L. H., Streit, A., & Debus, C. (2026, June). Combining Domain and Tensor Parallelism to Train Multi-billion-parameter AI Weather Models. In Platform for Advanced Scientific Computing Conference (pp. 1-13).
AbstractPhipps, K., Kuhl, M., Weiel, M., Busch, M., Lewen, J., Blumenröhr, N., ..., Götz, M., Pargmann, M. (2026). The PAINT database for operational concentrating solar power plant data following FAIR data principles. Nature Energy, 1-10.
AbstractLaufer, F., Götz, M., & Paetzold, U. W. (2025). Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication. Energy & Environmental Science, 18(4), 1767-1782.
AbstractDebus, C., Piraud, M., Streit, A., Theis, F., & Götz, M. (2023). Reporting electricity consumption is essential for sustainable AI. Nature Machine Intelligence, 5(11), 1176-1178.
AbstractGötz, M., Bodenstein, C., & Riedel, M. (2015) HPDBSCAN: highly parallel DBSCAN. In Proceedings of the workshop on machine learning in high-performance computing environments (pp. 1-10).
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