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block within this process. You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to develop the deep-learning models while having
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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perturbations. Climate and human-induced disturbances may alter forest ecosystems dynamics, modifying eventually their resilience to rapidly changing conditions. Previous studies based on the temporal analysis
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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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of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and develop their individual
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susceptible to SM, VWC, and atmospheric delay. As a result, the objective of this PhD project is to develop models able to fuse backscattering and phase information to estimate SM and VWC more accurately. The
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a powerful way for assessing forest stress and disturbances over large areas and to monitor forest vitality over time. This research uses remote sensing technologies together with physical models and
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cytotoxic payloads (Trastuzumab Deruxtecan and Trastuzumab Emtansine). These models will allow us to explore how payload differences influence both tumor cell killing and immune modulation within the TME. In
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, including weather, climate and sea state forecasting. The Air-Sea Fluxes group at the Institute of Coastal Ocean Dynamics conducts laboratory- and field-based research, by developing and deploying unique
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in