PhD position (f/m/d) Cross-Sensor Transfer Learning for Long-Term Forest Biomass and Structure Estimation Using Established and Next-Generation SAR Missions Part-time

PhD Position at the Institute of Photogrammetry and Remote Sensing (KIT-IPF)

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.

Your Tasks

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:

• Developing cross-sensor transfer learning frameworks based on multi-level SAR observables for above-ground biomass estimation by exploiting polarimetric SAR features across sensor generations and enriching them with higher-order structural information from Polarimetric InSAR and Tomographic SAR where available.
• Exploration of multi-modal and multi-model support using optical and hyperspectral data to improve robustness and generalizability of transfer learning between SAR sensors.
• Sensor-aware uncertainty characterization and error transfer, with emphasis on decomposing the error budget and estimating loss of precision associated with products derived from established missions compared to new biomass missions.
• Validation across long-term forest observatories in multiple regions, including Europe (TERENO), Australia (CSIRO Permanent Rainforest Plots), and potentially Brazil, ensuring transferability across forest types, climatic zones, and land-use contexts relevant to C4LAND.

Your Profile

• An above-average M.Sc. degree (or equivalent) in remote sensing, environmental sciences, computer science, forestry or a related field.
• Strong background and interest in Earth observation, remote sensing of forests and SAR data processing
• Experience with machine learning, domain adaptation, or transfer learning methods is highly desirable.
• Excellent communication skills and enthusiasm for working in an international and multidisciplinary research environment.
• Fluency in English, written and spoken
• Willingness to undertake a one-year research placement at the University of Melbourne, Australia and comply with formalities at both institutions.

We Offer

• Sciene for Impact: Engage with topics of societal relevance — in an excellent scientific environment that enables change.
• Flexible Working Hours: Take advantage of flexible‑hours schemes, remote‑work options, part‑time models, and a 30‑day annual leave entitlement to achieve an optimal work‑life balance.
• Career‑Building and Developmental Opportunities: We provide you with a structured onboarding program, a broad spectrum of continuing‑education options, and personalised support, thereby fostering your individual growth.
• Family-friendliness: The “KIT‑Family +” program assists you in reconciling work and family life by offering childcare services, holiday activities, a parent‑child office space, and assistance with caring for relatives.
• Stay Healthy: Under the motto “Fit at KIT – Body, Mind and Soul,” we promote your well‑being through fitness classes, mental‑health programmes, and regular preventive health examinations.
• Individualised Extra Benefits: Enjoy a corporate pension (VBL), a €25 monthly contribution toward a JobTicket BW, plus a broad selection of cultural and recreational programmes.

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