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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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, remote sensing observations, and prediction and valuation of forest functions, providing a holistic view on forests as complex systems. See the project website to find out more about the FORFUS doctoral