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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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Amazonian soils; and (3) how the JULES land surface model can be improved using novel field and experimental data. The doctoral researcher will shape the project, lead field experiments in southern Amazonia
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substantial intensification of the ocean heat transport, highlighting their climatic influence. However, the dynamics of submesoscale flows, and hence their representation in climate models, have not been
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into rivers to restore natural processes, are increasingly employed to address habitat degradation, biodiversity loss, and flood-risk. This PhD aims to monitor and model the ecological responses of plant and
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, species distribution modelling, stomach content and dietary isotope analysis, and ecosystem modelling. Useful recruitment links: For information relating to the research project please contact the lead
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spans animal evolutionary ecology, molecular ecology, and modelling of complex systems, and obtain interdisciplinary training in state-of-the-art approaches and techniques, which are highly south-after by
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modelled using UK-based case studies, selected from a shortlist in Isle of Portland, S Wales, SW England, and the Peak District. The work will be supported by Deep Digital Cornwall at Camborne School
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of their impacts and how to quantify them. There have been attempts to estimate impacts based on empirical approaches (e.g. Bradshaw et al. 2022), while dynamic process-based models are also known
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hierarchical models and existing minimum inhibition concentration data (the lowest concentration of an antimicrobial at which microbial growth is inhibited) to refine suggested regulatory targets; Complementary
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in relevant cellular and in vivo models. Depending on progress, it is expected that you will present your research at national and/or international major research conferences. A suitable candidate will