Area of research:
Scientific / postdoctoral posts
The “Causal Inference” group at German Aerospace Center’s Institute of Data Science develops theoretical foundations, algorithms, and accessible software tools for causal inference and machine learning and closely works with domain experts, especially in the climate sciences. Causal inference is a challenging and promising research field and its application to domains such as climate science will have a high impact both to advance science and to address topics of critical importance for the society. The core methodological topics include causal inference and causal discovery for spatio-temporal dynamical systems, machine learning, deep learning, and nonlinear time series analysis. But the methods are flexible and open for your ideas!
The position is part of the EU project XAIDA together with several EU partners. The goal is the development of causal inference methods to better understand the causes of extreme events (heat waves, rainfall, etc.) from observational and model data.
- Development of theory and methods for causal inference and machine learning
- Implementation of methods in well-documented software
- Collaboration in the application of methods in different domains, in particular climate science
- Publication of results in peer-reviewed journals
- Presentation of results on national and international conferences
To support your international research experience, the group has a generous travel budget for conferences and extended research stays. Currently, we have collaborators at Imperial College London, Oxford, Carnegie Mellon University, National Center for Atmospheric Research (NCAR), and California Institute of Technology, and more.
This research center is part of the Helmholtz Association of German Research Centers. With more than 42,000 employees and an annual budget of over € 5 billion, the Helmholtz Association is Germany’s largest scientific organisation.