PhD Project

**Job Description**
This PhD project aims to develop models that fuse backscattering and phase information from microwave remote sensing measurements to accurately estimate surface soil moisture (SM) and vegetation water content (VWC). The research will investigate how canopy penetration depth varies with wavelength and how phase differences in Synthetic Aperture Radar (SAR) acquisitions are affected by SM, VWC, and atmospheric delay. The outcomes will lead to improved retrieval of soil moisture in vegetated areas at high spatial resolution and enhanced estimation of plant water content, supporting operational services in precision agriculture and forest management, and contributing to a better understanding of forest ecosystem resilience and vulnerability. This position is part of the FORFUS doctoral training unit and associated with the FORLUX research project.

**Skills & Abilities**
• Strong interest in modelling, model-data integration, and remote sensing data analysis.
• Knowledge of programming, remote sensing, and electromagnetism would be an advantage.
• Proficiency in written and spoken English.
• Strong potential to excel in a collaborative and multidisciplinary environment.

**Qualifications**
Required Degree(s) in:
• Engineering
• Mathematics
• Physics
• Remote Sensing
• Machine Learning

**Experience**
Other:
• Your master diploma has to be recognized in Luxembourg.

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