This assignment focuses on advancing operational forest disturbance monitoring using radar satellite data and contributing to global efforts to understand and combat forest loss. The position combines methodological research with the further development and operation of a radar-based forest disturbance alert system, using established and newly available radar satellite missions.
Radar remote sensing is particularly valuable for forest monitoring because of its sensitivity to vegetation structure and its ability to provide systematic observations largely independent of daylight and cloud cover. However, translating these observations into accurate, consistent, and operational information on forest disturbance and post-disturbance regrowth remains challenging. Radar responses are influenced by wavelength, acquisition geometry, forest structure, seasonality, and environmental conditions.
The project will address these challenges in two main directions. First, it will extend the framework from disturbance detection towards continuous forest change monitoring, including the detection and characterization of post-disturbance forest regrowth and structural recovery. Second, it will expand the geographic applicability of the alerts beyond their current implementations.
The position is embedded in a strong international and interdisciplinary research environment, including collaboration with international partners. The project provides opportunities to contribute to high-impact scientific publications, international collaborations, and operational monitoring products with direct societal relevance.
What you do
- Operational lead of the near-real-time alert system
- Developing and implementing radar-based methods for detecting and monitoring post-disturbance forest regrowth and structural recovery
- Studying forest disturbance-recovery dynamics
- Coordinating project deliverables and ensuring their timely and high-quality completion
- Contributing to collaborative research in an international consortium
- Publishing research results in leading peer-reviewed journals
- Communicating research findings and monitoring developments to scientific, policy, and stakeholder audiences
Profile
- Highly motivated and independent researcher with strong methodological skills
- Demonstrated expertise in (radar) remote sensing
- Clear interest in multidisciplinary research at the interface of remote sensing, forest ecology, and forest management
- Combines scientific rigor with the ability to work collaboratively in an international research environment
- Able to translate methodological advances into robust monitoring approaches
What they ask
- A PhD in remote sensing, data science, forest ecology or a related field
- A strong foundation in remote sensing, including demonstrated experience with radar/SAR remote sensing
- Experience with forest change, disturbance, regrowth, or recovery dynamics
- Experience developing reproducible data-processing and analysis workflows
- Excellent programming skills, with strong proficiency in Python
- The ability to publish scientific research and communicate results effectively
- Experience working in interdisciplinary teams and with stakeholders
- Excellent written and spoken English skills (C1 level)
Preferences.
- Experience with satellite time-series analysis, change detection and near-real-time forest monitoring
- Experience with various spaceborne radar missions
- Experience with scalable Earth observation processing using cloud-computing or high-performance computing environments
- Knowledge of forest disturbance ecology, forest management, and post-disturbance vegetation dynamics
- Experience engaging with operational users, policy actors, or other stakeholders
Practical
- You will work as part of a research team within a specialized laboratory for geo-information science and remote sensing. The team studies human activities and the dynamics of forest ecosystems from regional to global scales using radar remote sensing. Its work focuses in particular on fundamental radar and multi-sensor methods, near-real-time change monitoring, and the integration of satellite observations with in-situ field data.
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