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systems are built upon fixed ground infrastructure, such as terrestrial base stations. This stationary deployment lacks the necessary flexibility to adapt to dynamic environments. In scenarios where ground
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assess tipping points in natural systems has profound implications for how humanity responds to climate change, manages land, and prepares for geohazards. This project bridges mathematics, earth science
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Funded PhD Studentship in applied geospatial ecology. This project will investigate how dryland vegetation productivity varies across space and time and how it responds to land management. Working
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plausible land-use changes, with uncertainty explicitly represented to evaluate robustness rather than single-point forecasts. Third, a preference-learning reinforcement learning agent will propose
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membranes will also be considered, ensuring relevance to future industrial deployment. This research is inherently interdisciplinary, combining elements of materials science, solid-state chemistry