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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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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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. Surface deformation during volcanic unrest has begun to be explored using models based on magma migrating and accumulating in a magma-mush reservoir, but they have limitations and have not been linked
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climate models, including the UK Earth System Model (UKESM), resulting in critical gaps in both seasonal forecasts and long-term climate projections. This PhD will develop a new parameterisation of snow
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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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satellite SAR, LiDAR and/or optical imagery to enable rapid, safe, and scalable assessments of damage. Candidate methods for temporal modelling and anomalies detection, which are likely to occur at affected
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recombination, maintaining genetic linkage of toxin/antitoxin-like systems. As a result, these chromosomes accumulate deleterious mutations that are unaccounted for in existing gene drive models. The student will
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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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, encompassing advanced geospatial analysis, remote sensing methods, atmospheric transport modelling, and epidemiological data integration. The researcher will also receive guidance in handling large datasets
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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