Postdoctoral Research Fellow - Graph Learning

Postdoctoral Research Fellow - Graph Learning

Max Planck Institute of Biochemistry

Martinsried, Germany

Project overview

This position offers a rare opportunity to contribute to the future of machine learning for graph-structured data. While machine learning has transformed fields such as computer vision and natural language processing, graph-structured data remain a rich frontier for methodological innovation, with many fundamental challenges and exciting opportunities ahead. The successful candidate will pioneer new algorithms for graph-structured data, revisit classical graph problems through the lens of modern machine learning, and help define the next generation of generative models for graphs. As part of Karsten Borgwardt's Department of Machine Learning and Systems Biology, these methodological advances will provide the foundation for a broad range of downstream analyses while also being applied directly to frontier problems in biology using unique, large-scale experimental datasets generated across the Max Planck Institute of Biochemistry.

Qualifications

The successful applicant will hold a Ph.D. in computer science, machine learning, bioinformatics, or related fields. The applicant should have a strong interest and prior experience in developing machine learning methods, ideally with a background in graph-based or geometric deep learning. Written and oral command of English is essential.