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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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-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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. A background in forest engineering, ecohydrology, and/or ecology would be ideal but is not required. Experience in numerical modelling, big data analysis, and plant ecological and physiological
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motivated and talented doctoral candidate to work on the biophysics of host-pathogen interactions using in vitro model systems mimicking chronic diseases. The project foresees ample collaborative
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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
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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
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, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs … How to apply Applications, written in English, should include: A detailed CV A list of
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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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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