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materials that combine the structural features and mobility in a single component. These are liquid crystal elastomers (LCEs), unique responsive materials that reversibly change shape, colour, adhesive
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collaborative opportunities globally, as well as within Luxembourg. The doctoral candidate will perform wet lab experiments as well as computational analysis to uncover biophysical principles that regulate host
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disturbances on OFR using visual media (e.g., photos, videos). The supply and demand of OFR will be assessed via a spatial analysis to identify recreational hotspots and inform forest management of them. Finally
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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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perturbations. Climate and human-induced disturbances may alter forest ecosystems dynamics, modifying eventually their resilience to rapidly changing conditions. Previous studies based on the temporal analysis
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well as computational analysis to uncover biophysical principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine
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environmental variables to develop LUE, WUE and canopy conductance function for diagnostic mapping of forest ecosystem functioning metrics. We will apply time-series analysis to investigate seasonal and
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. Activities of the successful candidate: Conduct extensive background literature analysis. Plan and organise experiments to define and test hypotheses and develop forefront research. Publish the results
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: · Processing multi- and hyperspectral satellite and drone data · Collection of field data on relevant forest traits and laboratory analysis · Forest radiative transfer modelling · Hybrid
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analysis (R, Python) is an asset. Curiosity, creativity, rigor, willingness to learn, team spirit and collaborative capacity, excellent time and priority management. Fluency in English (written and spoken