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Field
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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threats to biodiversity. Freshwaters are disproportionately affected by such invasions, and home to a disproportionately large proportion of biodiversity, especially invertebrates. They also provide
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wind farms in the UK and neighbouring countries is expected to triple in less than five years. Newer wind farms are also deploying very large turbines of 14 MW or more, meaning that wake effects between
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river channels, altering their topography, destabilising banks, and changing how water and sediment move through large rivers. While these impacts are becoming clearer, what remains poorly understood is
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writing and implementing code alongside extracting information, trends, and patterns from large datasets. Topics to explore during this PhD project include: Investigating available software options Methods
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narrow down what parts of our genome are actually important for defining modern human-specific biology. This project will analyse data from these ultra-large datasets, alongside data from our great apes
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undertaken in the NHS require a few days stay in hospital. However, it is possible to discharge some patients on the same day as their operation. We are preparing for a large randomised controlled trial by
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will use a high-fidelity large eddy simulation (LES) code and scientific machine learning tools, such as real-time optimisers, in order to simulate wind farms exposed to various atmospheric inflows. Some
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simulation workload and update the solver data structures when the mesh changes. These approaches would be applied on modern large-scale heterogeneous parallel computing environments where both CPUs and GPUs
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loads in power system dynamics and stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven