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

Please note: By clicking on the image area, you give your consent for video content to be reloaded from YouTube, for YouTube/Google cookies to be stored on your IT system and for personal data such as your IP address to be passed on to Google. If you click on another video after having finished watching the video content, YouTube will open in a new tab of your browser and will collect more data from you. Further information is provided in our data protection notice and under Google Privacy .

The Team

Publications

Contact

Math News

02-13-2023

From parent rocks to soil: Co-evolution of structure and function during pedogenesis

Call for Papers- Special Issue on Co-evolution of structure and function during pedogenesis

“From parent rocks to soil: Co-evolution of structure and function during pedogenesis” which will be published in the "Journal of Plant Nutrition and Soil Science" (J. Plant Nutr. Soil Sci.; ISSN: 1436-8730 (print), 1522-2624 (online))
We hope to collect a variety of attractive, stimulating, novel, and comprehensive original research papers and review articles on this exciting research topic. The deadline for submissions of manuscripts is March, 30^th, 2023.

The “Call for abstracts” and author guidelines can be found here:

https://onlinelibrary.wiley.com/journal/15222624/homepage/sipedogenesis?=

We cordially invite submissions from the various fields of soil research employing one or combinations of the variety of experimental, observational, instrumental, and computational methods and approaches that aim to contribute to a better understanding of the intricate pathways of the fascinating co-evolution of structure and function during pedogenesis.

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.