Causal discovery in the presence of multiple time scales


This is a theoretically and methodologically oriented project. You will develop and implement cutting-edge data-driven methods that will enhance our understanding of the complex Earth system. You will be part of a research group ( of physicists, environmental scientists, mathematicians, and computer scientists that work together on exciting topics in causal inference, machine learning, and dynamical systems for climate research.

Causal inference and discovery address the problem of learning cause and effect relationships from observational data. These methods help to deepen our understanding of complex dynamical processes in the climate system such as the interactions of aerosols and clouds. Those occur on a variety of time scales, which are all mixed in the observed large-scale time series signals. If not taken into account in causal inference, this mixing leads to temporal confounding across scales.

The project approaches this problem by combining causal inference with ideas from machine learning and nonlinear dynamics, such as Gaussian processes, wavelets, and other filtering techniques. To facilitate the development of innovative new methods, we will closely collaborate with our partners at the University of Valencia and use artificial data from the causality benchmark database. We will further collaborate with the group at the University of Edinburgh to also work with aerosol-precipitation interaction data simulated from actual climate models. Finally, the methods will be applied to detect and attribute the aerosol influence on global precipitation patterns in real-world observational data. Moreover, the student will utilize the methods for parameter estimation in operational remote-sensing products in a secondment at GAF AG.

This project plan is flexible and very much open to your own ideas! To grow as a scientist, we will provide you with the support you need but also grant the freedom you want. The project is hosted at the German Aerospace Center (DLR), which is Germany’s national research institution for aerospace, energy and transportation. Our group is based at the DLR Institute of Data Science in Jena, a lovely mid-sized university town that is home to various research facilities and technology-focused companies. DLR supports a healthy work-life balance, including remote working options.


Jakob Runge (supervisor), Joachim Denzler (co-supervisor), Gabi Hegerl (co-supervisor), Gustau Camps-Valls (co-supervisor), Sebastian Carl (GAF AG, non-academic advisor)


2 months at University of Valencia, 4 months at University of Edinburgh, 4 months at GAF AG

Enrolment in Doctoral degree

PhD in Data Science at University of Jena

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