Do you have a strong interest in remote sensing, forests, and Earth observation? Are you excited about combining next-generation satellite missions with machine learning to reconstruct long-term forest dynamics? If so, we invite applications for a PhD position at the Institute of Photogrammetry and Remote Sensing (KIT-IPF), as part of the International Research Training Group C4LaND.
Forests play a central role in the land-use nexus by providing carbon storage, biodiversity, and renewable resources, while being increasingly affected by land-use change and climate extremes. Robust, spatially explicit and temporally consistent information on forest biomass and structure is essential for assessing long-term land-use trade-offs and informing integrated modelling and governance frameworks. Recently launched Synthetic Aperture Radar (SAR) satellite missions have been specifically designed to retrieve three-dimensional forest structure and above-ground biomass with high sensitivity, but their observational records are short. In contrast, established SAR missions such as Sentinel-1 offer dense and consistent time series extending back more than a decade, albeit with limited biomass sensitivity.
This PhD position is part of the International Research Training Group C4LaND and focuses on developing transfer learning approaches that link forest above-ground biomass and structure estimates from next-generation, biomass-oriented SAR missions to long-term SAR archives, thereby enabling spatially explicit reconstruction of forest dynamics at least back to the beginning of the Sentinel-1 era. The project will be hosted at KIT (Karlsruhe Institute of Technology), Institute for Photogrammetry and Remote Sensing (IPF), under the supervision of Prof. Stefan Hinz. Your Melbourne co-advisor will be Dr. Jagannath Aryal.
Lines of research include:
Job location: Karlsruhe (and Eggenstein-Leopoldshafen)
Salary: Salary category 13 TV-L; classification is based on personal and professional qualifications.
Contract duration: limited for 3,5 years
Contact person in line-management: Stefan Hinz, stefan.hinz@kit.edu
If you have general questions about the application process, please contact Lea Jung Personalservice (PSE), lea.jung@kit.edu +49 721 608-42098
At KIT we value the diversity of our employees; different perspectives and backgrounds enrich our work. We prefer to balance the number of employees (f/m/d). Therefore we kindly ask female applicants to apply for this job. Applications from recognized severely disabled individuals are given preferential consideration when qualifications are equal. Application up to: Job posting number: 1114/2026
Tagged as: Life Sciences
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