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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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sources, and environmental concentrations have been shown to increase resistance. However, ‘safe’ release limits for antibiotics based on AMR endpoints are not mandated, partly due to lack of consensus
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, durability, and environmental sustainability, while addressing cost constraints and net zero objectives. It will include an in-depth review of shortcomings in current design, based on literature review and
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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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regions, and may have also been observed in historical trends, but the processes driving this delay are not well understood. This project will use observations and climate model simulations to examine how
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structural biology), Moebius (host–virus evolution, mathematical modelling), and Verkade (correlative microscopy, VolumeEM), the student will be trained in cutting-edge techniques, including cryo-electron
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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sediment–water column model we have been developing in Exeter. With a well calibrated and tested sediment/water-column model, we will then perform idealised experiments focused on a range of proposed blue