Postdoc in Data-driven Methods for Remote Sensing of Forests Using the Biomass Satellite

Join the Department of Space, Earth and Environment at the University of Gothenburg for a postdoctoral position focused on advancing data-driven methods for remote sensing of forests using the European Space Agency’s Biomass satellite. The Division of Geoscience and Remote Sensing is renowned for its expertise in space, geoscience, energy, and sustainability, and is committed to curiosity-driven research, education, and global collaboration. The Biomass satellite, launched in April 2025, is equipped with cutting-edge P-band synthetic aperture radar (SAR) and will deliver global forest biomass data through its polarimetric and tomographic radar capabilities. This five-year ESA Earth Explorer mission addresses critical needs in environmental monitoring and sustainable development. As a postdoc, you will contribute to the development and extension of the biomass algorithm, integrating machine learning approaches to enhance the accuracy and scope of biomass data products. The project involves generating large-scale training datasets from various remote sensing sources, including airborne and spaceborne laser scanners, and validating results with international ground-truth networks such as GEO-TREES. You will be responsible for developing, implementing, optimizing, training, testing, benchmarking, and validating machine learning algorithms for above-ground biomass density estimation. Additional tasks include processing large remote sensing datasets, preparing AI-ready data, presenting research at conferences, publishing in scientific journals, and supervising students. The position offers a dynamic and inspiring work environment in Gothenburg, Sweden, with full employee benefits provided by Chalmers University of Technology. Chalmers is dedicated to gender balance, equality, and inclusion, and offers Swedish language courses to help international staff settle in. The postdoc contract is for three years, requiring physical presence throughout the employment. Applicants must have a doctoral degree in remote sensing, physics, electrical engineering, or related fields, strong English communication skills, and experience with remote sensing data and machine learning algorithms. Preferred qualifications include recent doctoral completion, experience with forest mapping, large geo-spatial datasets, SAR and lidar data, and university-level teaching. To apply, submit your application in English via the online form, including a comprehensive CV, publication list, teaching experience, and a personal letter outlining your research background and future goals. The deadline for applications is 27 March 2026. For further information, contact Prof. Lars Ulander at [email protected]. This position is ideal for candidates seeking to advance their careers in academia, industry, or the public sector, and to contribute to global environmental monitoring and sustainable solutions.