Vacancies

Tenure Track position in Scientific Computing of Technical Plasmas and/or Fluids

The Multiscale Dynamics group develops methods of scientific computing, nonlinear dynamics, model reduction and machine learning with a particular focus on the multiscale plasma dynamics in lightning and space weather, and in high voltage and plasma technology. 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 and NASA.

PhD Student 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.

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.