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short-term physiological responses of tree species and modified long-term dynamics of the whole ecosystem. On the other hand, vegetation demography models are numerical tools formulating forest processes
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the current age lies in joining forces from multiple disciplines to focus on understanding causal and mechanistic links between the microbiome and chronic diseases alongside generalisable pathogenic effects
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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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, the valuation of other ES will be performed based on benefit transfer methodologies, considering forest function/flow values obtained in other research context to put into context the value of OFR
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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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cybersecurity allowing thus to validate and receive feedback from on-the-field cybersecurity practitioners. As generative AI (GenAI) platforms and large language models (LLMs) are increasingly integrated
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in a structured doctoral training environment. The need of microbiome research in the current age lies in joining forces from multiple disciplines to focus on understanding causal and mechanistic links
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the advancements brought by AI, there is currently no tool sufficiently intelligent to fully aggregate and utilize diverse data sources to create a comprehensive and adaptive dashboard for taking
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security