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methods: The Met Office aims to help its customers stay safe and thrive by producing reliable weather and climate information. Numerical models of the climate system are an important tool to help achieve
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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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and night-time low temperatures. There is a need to improve the way the stratified boundary layer is represented (parametrized) in these simulations and also interrogate the models with high-quality
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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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for rare or threatened animals (the “long-tail” problem). Moreover, current models give little indication of when they might be wrong, restricting their use in conservation. This PhD will tackle both issues
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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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this information affect behavioural decisions before and during combat? This PhD project will tackle these questions using a combination of evolutionary modelling and behavioural experiments in a wild population of
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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