Postdoc Position: Explainable AI × Causal Inference (CRC 1294, University of Potsdam)
Together with Marina Höhne, we are looking for a postdoctoral researcher to join project B12 of the Collaborative Research Centre CRC 1294 “Data Assimilation” at the University of Potsdam.
The project
The project brings together two fields that have a lot to say to each other. On the causal side, it focuses on causal discovery and effect estimation, including approaches based on prior-data fitted networks (PFNs). On the XAI side, it addresses concept-based explainability and the study of causal mechanisms inside neural networks. The goal is to develop methods that make machine learning models more interpretable through a causal lens, and that use modern deep learning to advance causal inference.
Your profile
- PhD in machine learning, statistics, computer science, physics, or a related field
- Strong background in machine learning
- Experience in either explainable AI or causal inference, and interest in exploring the other
- Experience with time series data is a plus
What we offer
- Full-time position, salary according to E13 TV-L
- Funded until June 2029
- Pure research position with no teaching obligations
- An interdisciplinary research environment within CRC 1294, spanning mathematics, statistics, and the geosciences
- Close collaboration between the AI in the Sciences group (Jakob Runge) and Marina Höhne’s group
How to apply
Please send your CV, a short statement of your research interests and background, and the contact details of two references to jakob.runge@uni-potsdam.de. Applications will be reviewed on a rolling basis until the position is filled. Informal inquiries are welcome.
