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

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Math News

01-29-2024

German Mathematical Society awards DMV Student Conference 2026 to the KU

The Executive Committee of the German Mathematical Society (DMV) decided that the Student Conference (StuKon) 2026 will be hosted by the KU at the MIDS in Ingolstadt. "Mathematics at the KU Eichstätt-Ingolstadt is very pleased about the award, and even more looking forward to a great event with the best graduates of mathematics courses in Germany!", comments initiator Prof. Dr. Marcel Oliver.

At the Stukon, students and graduates of mathematics degree programs (Bachelor's, Diplom, Master's or state examination) have the opportunity to present their final theses from the previous year. All applications received are evaluated by a jury of experts and a number of them are then given the opportunity to present them to a wide audience. Various prizes are awarded. In addition to the presentations by graduates, there will be specialist presentations by scientists, tours of the institute and workshops on job opportunities.

All information about the Stukon and how to register can be found HERE.

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.