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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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acceptability. D2ET will develop a comprehensive digital platform for planning energy transition scenarios, leveraging a consolidated data model and advanced analytics to facilitate strategic decisions with
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personalized immunotherapies for cancer patients with brain metastasis. The TCI group works at the interface between immunology and cancer biology and combine the use of preclinical models of cancer and human
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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
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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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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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below? Are you our future colleague? Apply now! Experience and skills · You have a strong interest in terrestrial ecosystems modelling, vegetation demography, plant physiology, and climate change
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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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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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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