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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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Math News

Successful PhD Defense of Dr. Johannes Gückel

We are pleased about the successful PhD defense of Johannes Gückel.

Under the supervision of Prof. Pirmin Fontaine, Johannes made a significant contribution to the field of urban logistics and collaboration among logistics service providers. He developed innovative mathematical models, metaheuristics, and machine learning approaches to design efficient and fair cooperative city logistics systems. His work particularly addresses network design, fair cost allocation, and the practical implementation of collaboration concepts. The results demonstrate how efficiency and fairness can be jointly considered in urban freight systems. Johannes now continues his career as a Data Scientist at dm-drogerie markt. We wish him all the best for his future career.

His dissertation resulted in four scientific articles, which have been published or accepted for publication in renowned academic journals:

  • Gückel, J., Crainic, T.G., & Fontaine, P. (2026). A two-step large neighborhood search for a collaborative two-tier city logistics system. European Journal of Operational Research (VHB: A).
  • Gückel, J., Crainic, T.G., & Fontaine, P. (2025). Tactical planning in cooperative two-tier city logistics systems with fairness constraints. Sustainability Analytics and Modeling (VHB: B).
  • Gückel, J., & Fontaine, P. (2025). Fast Shapley Value Approximation Through Machine Learning With Application in Routing Problems. Networks (VHB: A).
  • Gückel, J. (2025). Approximating Shapley values with subcoalition Shapley values in routing problems. Transportation Research Procedia (VHB: C).

Mathematical Institute for Machine Learning and Data Science

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