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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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Area of research: Scientific / postdoctoral posts Starting date: 14.08.2025 Job description: GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt operates one of the leading particle
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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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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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the FORLUX (https://www.list.lu/en/research/project/forlux ) research project, both of which together will include 13 doctoral candidates and 4 postdoctoral researchers. We seek candidates with a strong
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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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practices. Within this framework you will: extend and use a process-based modeling approach which explicitly represents microorganisms and biomolecule functioning in soil systems. use process-based modeling
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, a state-of-the-art process-based model for groundwater risk assessment and contaminant transport modeling. By improving predictive modeling of transient contaminant source terms, this research will
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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs
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cells, embryo models and human embryos, with a focus on understanding how cell stress impacts these processes. During your PhD, you will work very closely with a postdoctoral fellow that will be carrying