Table of Contents

Research

Experience and interests

My general research interests are scientific computing, computational (plasma) physics and more recently also machine learning.

I have developed both particle-in-cell and plasma fluid codes for the simulation of electric discharges (non-thermal plasmas). I'm also active in the development of MPI-AMRVAC, a framework for (magneto)hydrodynamics simulations. My work has focused on topics such as adaptive mesh refinement (AMR) and fast elliptic solvers (e.g., multigrid). I like to study systems that have some intrinsic complexity, not coming from boundary conditions or input data.

Since 2018, I have been working on machine learning methods applied to space weather applications, in collaboration with Enrico Camporeale, as I have taken over two EU projects in this direction from Enrico (AIDA and ESCAPE, see below). Current research focuses on forecasting time-series data, recognizing magnetic reconnection, and the use of unsupervised methods for e.g. dimensionality reduction and clustering.

Research projects

(Past projects in italics)

Supervision

Past postdocs, PhD students etc. in italic

Postdocs

(In collaboration with Enrico Camporeale)

PhD students

I (co-)supervise(d) the following PhD students together with Ute Ebert:

PhD commitee member

MSc / BSc students

Ideas for research projects

Examples of research/project ideas: