Doctoral Candidate / PhD Position

**Job Description**
The project focuses on Earth Observation (EO) science to assess forest stress, disturbances, and vitality using remote sensing, physical models, and machine learning. It investigates how ecosystem water stress and drought affect forest ecosystem functioning and aims to advance the assessment of forest vitality and stress propagation. A key focus is understanding Water Use Efficiency (WUE) and Light-Use Efficiency (LUE) to manage carbon and water trade-offs in forests under varying environmental conditions.

**Skills & Abilities**
• Strong interest in land-atmosphere interaction, evapotranspiration, photosynthesis, biometeorology, and climate.
• Background in remote sensing and ecohydrology (ideal but not required).
• Experience in big data analysis, data science methods, Machine learning, and/or artificial intelligence (strong asset).
• Enjoy modeling, model-data integration, data analysis, and handling large eddy covariance flux databases.
• Essential scripting and programming experience.
• Ability to work in a team, explain, and present research ideas and results.
• Fluency in English, both oral and written.

**Qualifications**
Required Degree(s) in:
• Environmental Science
• Forestry
• Civil and Environmental Engineering
• Remote Sensing
• Geosciences

**Experience**
Other:
• Master’s diploma must be recognized in Luxembourg.

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