PhD scholarship

PhD opportunity in Implicit Neural Representations in Earth Observation at the Machine Learning in Earth Observation Laboratory, University of Bonn, Germany. The project sits at the intersection of computer science, earth science, environmental science, geography, and statistics, with a strong focus on geospatial AI, remote sensing, GIS, and multimodal spatial-temporal data.

The successful doctoral researcher will develop machine learning methods for satellite imagery, environmental variables, GIS layers, and other geospatial data sources. The work includes building reusable benchmarks, contributing open-source research software, and publishing at machine learning venues. The position is embedded in existing collaborations and an interdisciplinary research environment.

Applicants should hold a very good Master’s degree in Computer Science, Geodesy, Remote Sensing, Machine Learning, Data Science, Geoinformatics, Computational Geoscience, Physics, Mathematics, or a related field. Strong Python skills, experience with PyTorch or JAX, and mathematical maturity in linear algebra, probability, and optimization are expected. Very good written and spoken English is required. Experience with implicit neural representations, neural fields, geospatial foundation models, uncertainty quantification, publications, or open-source software is advantageous.

The position is a full-time doctoral researcher role according to TV-L E13. It offers the opportunity to pursue a PhD (Dr.-Ing.) at the University of Bonn, with structured doctoral support, access to scientific networks, and support for conference participation and professional development. The anticipated start date is late summer 2026. Applications are reviewed on a rolling basis until the position is filled.

Apply in English via the online form and upload a single PDF containing a cover letter and CV. The cover letter should explain your motivation for pursuing a PhD in general and in this group, and include one research question or technical direction you would like to explore.