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

12-03-2024

Dissertation award for Dr. Alexander Rave

Dr. Alexander Rave honored with the dissertation award for the best doctoral thesis

Last Saturday, Dr. Alexander Rave received the award for the best doctoral thesis at WFI. In January, he successfully defended his dissertation titled "Mathematical models and metaheuristics for optimizing the transportation planning of drones: Case studies in parcel delivery and healthcare services" with the highest distinction (summa cum laude).

In his research, he developed mathematical models and efficient algorithms for the usage of drones. His work explored not only their use in package delivery in rural areas but also in the medical field to improve the availability of medications.

He was awarded the prize, sponsored by the City of Ingolstadt. Unfortunately, he was unable to attend this year’s graduation ceremony, so the certificate was presented to him shortly afterward by his doctoral advisor, Prof. Pirmin Fontaine.

We are delighted about this recognition and are thrilled that he will continue to work with our department as a postdoc.

Congratulations!

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.