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

11-27-2024

Tourism Technology Festival

On November 9 and 10, the Tourism Technology Festival 2.0, a hackathon, took place in Salzburg, Austria.

Denis Hoti, Ali Guliyev, Lukas Lang, Jakub Wisniewski, Southik Banarjee and Veronika Rybak from the KU's Data Science program took part in this event.

In total, there were five different tasks on the topic of tourism. The KU team chose the challenge “Travel personalization by culture and personality” and solved the task by implementing AI and machine learning solutions. They generated a model that they were able to train using the data provided. They developed a solution that shows tourists the best travel route for Austria.

After 24 hours of programming, a 15-minute interview is conducted by the jury. In the end, the KU students won the “Best Open Source” prize. This is awarded to the team with the best technical performance and the best documentation.

Congratulations from the entire MIDS!

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.