PhD Position in Contrastive Learning and GeoAI at Technical University of Munich Technical University of Munich in Germany

Technical University of Munich (TUM) is advertising a PhD / doctoral candidate research associate position in Contrastive Learning and GeoAI within the Professorship Big Geospatial Data Management.

The project focuses on hard negative sampling for contrastive representation learning and its applications in geospatial artificial intelligence. Research topics include developing sampling strategies that go beyond embedding similarity by integrating domain knowledge such as spatial distance, sensor metadata, and existing maps. A second part of the project explores how these strategies can identify informative subsets (coresets) of large geospatial datasets.

Application domains mentioned include cross-view geo-localization and visual place recognition using aerial, street-view, and LiDAR data. The professorship emphasizes large-scale georeferenced data, distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms.

Eligibility highlights: a completed master’s degree in mathematics, computer science, physics, geoinformatics, data science, or related fields; strong machine learning background; very good programming skills (Python, C++, etc.); fluent English; German is a plus. The team values independent work, willingness to learn, and interest in an international research environment.

Funding: full-time research associate position (100%, TV-L E13) for 3 years, funded by the German Research Foundation (DFG). The post includes the opportunity to pursue a doctoral degree.

Application deadline: 1 August 2026. Start date is expected between September and November 2026.

How to apply: prepare a single PDF with motivation letter, CV, degree certificates, transcripts, employment certificates, and any other relevant documents. Email the application to [email protected]. For questions, contact Prof. Dr. Martin Werner.