Welcome to the page of the Professorship for Data Assimilation

In order to be able to predict severe weather events or the melting of ice in the Arctic, information in the form of heterogeneous data must be linked with numerical models of dynamic systems. This is done through data assimilation, which makes it possible to better investigate processes and predict their further development. In the field of data assimilation, the professorship is concerned with the further development of data science algorithms by incorporating physical conservation laws, and solving correspondingly large optimisation problems in the environmental sciences. Quantifying the uncertainties of predictions, numerical models and observations also plays a central role here.

About us

Prof. Janjic introduces herself and the chair

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The Team

Publications

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Math News

10-06-2025

Successful PhD Defense of Dr. Daniel Müllerklein

We are happy to celebrate the successful PhD defense of Daniel Müllerklein.

Under the supervision of Prof. Pirmin Fontaine, Daniel made significant contribution to the field of resilient supply chain management. He developed innovative data-driven solution approaches for strategic, tactical, and operational decision making. He applied advanced combinatorial optimization techniques, e.g. Benders decomposition, novel machine learning algorithms, and heuristics. His applications on real-world data showed the practicability of his methodological framework. Daniel is now continuing his career in the family-owned Borne Group as CEO. We wish him all the best for his future career and always a resilient supply chain in his business.

His PhD thesis resulted in four papers. One is already Published in the European Journal of Operational Research (VHB: A) and two are currently under review in two top-journals (both VHB: A):

Müllerklein, D., & Fontaine, P. (2025). Resilient transportation network design with disruption uncertainty and lead times. European Journal of Operational Research, 322(3), 827-840.
Müllerklein, D., Fontaine, P., & Ortmann, J. (2025). A Cost Focused Machine Learning framework for replenishment decisions under transportation cost uncertainty. Working Paper.
Müllerklein, D., & Fontaine, P., 2025. On product characteristics in a two-echelon resilient network design. Working Paper.
Müllerklein, D., 2025. A seven-step supply chain resilience framework for mitigating waterborne disruptions in transportation network design. Working Paper.

MIDS seminar

Mathematical Institute for Machine Learning and Data Science

The Heisenberg professorship is part of the Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.