Leader of the group Multiscale Dynamics: Ute Ebert.

Nature and technology are full of dynamics, often involving multiple scales in length, time and energy. To model these processes, we combine scientific computing with model reduction and machine learning, with particular focus on plasma dynamics in lightning and space weather, and in high voltage and plasma technology.

Our research addresses questions in nature such as start and propagation of lightning strokes, terrestrial gamma-ray flashes and space weather, and closely related technological problems such as switch gear for long-distance electricity nets, air purification and disinfection with corona reactors, and protection of satellites and electricity nets from space weather.

Within national and European projects, we collaborate with colleagues in applied plasma physics, electrical and mechanical engineering, atmospheric electricity, and cosmic particle and space science, and with non-academic partners such as ABB, DNV-GL, ESA-ESTEC and NASA.

Find more about our work (including publications) on the personal homepages of the staff scientists:

and on the page with our numerical codes for Multiscale Plasma Dynamics.

View a photo of the Multiscale Dynamics group.




Postdoc on the subject of Machine Learning and Space Weather

The position involves research in machine learning techniques and Bayesian inference, applied to real-time forecasting of energetic electrons in the Earth radiation belts. These electrons can be harmful to satellites, whose disruption can potentially lead to catastrophic societal and economic events. The emerging field of Space Weather is concerned with making accurate predictions of such dangerous events, sufficiently in advance so that countermeasures can be taken. The aim of this project is to advance our space weather prediction capability by enhancing physics based models with a new data-driven probabilistic framework. The project will involve state-of-the-art numerical simulations, Bayesian parameters estimation, uncertainty quantification and machine learning techniques.



Associated Members