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

Contact

Math News

12-18-2024

Award for MIDS Professor

The magazine “Wirtschaftswoche” has recognized the most research-intensive business economists in 2024. Prof. Dr. Heinrich Kuhn, Prof. Dr. Thomas Mählmann and Prof. Dr. Pirmin Fontaine from the KU made it into the ranking.

The magazine collected the scientific articles of economists working at chairs, Fraunhofer and Max Planck Institutes in Germany, Austria and Switzerland. Prof. Dr. Pirmin Fontaine, holder of the Chair of Logistics and Operations Analytics, came 45th in the “Young Stars of Business Administration” category for economists under 40.

Prof. Fontaine is also a member of the Mathematical Institute for Machine Learning and Data Science. In his research, Fontaine focuses in particular on the development of solution methods for problems in the fields of mobility, supply chain management, production and logistics. He and his team are currently working on increasing resilience in the supply chain with the help of machine learning, improving on-call bus systems and planning urban logistics concepts, among other things.

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.