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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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The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg National Research Fund. MICRO-PATH, or Pathogenesis in the Age of the Microbiome (https
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order to better understand, explain and advance society and environment we live in. Your role The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg
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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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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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training unit: https://www.list.lu/en/research/project/forfus Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? Your PhD work will focus on outdoor forest
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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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, or multi-physics simulation. Experience and skills · Ideally 3–5 years of experience (including PhD) in one or more of the following: o Finite Element Modelling (FEM), o Multiphysics
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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