Postdoc on Foundation Models, Graph and Geometric Deep Learning, Causal Modeling

We are looking for an outstanding and highly motivated postdoctoral researcher to join our group and help lead the development of next-generation AI and machine learning methods.

The position focuses on foundation models, graph neural networks, geometric deep learning, and causal learning, with applications in high-impact biomedical settings such as immune system modeling, neurodegenerative diseases, and related areas.

Our group works closely with international partners, including researchers at Stanford, MIT, McGill, and others, and connects methodological AI research with cutting-edge screening technologies and pressing biomedical challenges. This is an opportunity to shape ambitious research and drive real-world impact at the interface of machine learning, biology, and medicine, with strong potential for scientific visibility and impact.

The position also offers the opportunity to collaborate closely with world-leading experts in immunology and human pregnancy at UKE Hamburg and to be tightly integrated into the DFG CRC 1713 on Maternal Immune Activation.