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processes, 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
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one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative
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TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe
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on characterizing forest structure and biodiversity via Unsplash Professional qualifications (required) Master’s degree in machine learning, computer science, or a forest-related field with a focus on remote sensing
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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empirical and remotely sensed data Compilation of data for running a simulation model, model evaluation against independent data sources Assessment of future forest development under different management
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research in fields such as remote sensing, communications technology, autonomous platforms, power electronics, energy systems and advanced control. More information on the research group can be found here
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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
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degree in atmospheric science, physics, environmental sciences, remote sensing, or a related field. Strong analytical and problem-solving skills, with interest in linking satellite data and atmospheric
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting