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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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Theoretical models for gravitational wave signals emitted by coalescing compact binaries are the cornerstone of modern gravitational wave astrophysics. Among the most pressing challenges
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anthropogenic activities, as well as limitations of existing models in effectively integrating human data to quantify human influence. Foundation AI models offer significant potential due to their strength in
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our ability to predictably control and exploit the drop for useful tasks. The proposed project has two aims: First, to develop computational models to quantitatively predict the response of chemically
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systems, providing early detection of adverse events such as infection and inflammation. The project will involve sensor design and modelling, prototype development, electrochemical characterisation, and
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frameworks that can maximise the performance, efficiency, and emissions reduction potential of such new fuels through intelligent design, modelling, and experimental validation. Research Objectives Investigate
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the spatial distribution of woodburning emissions. Integrate observations into inversion modelling to refine regional and national emission inventories. Model the impact of woodburning on UK air quality and
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Cardiometabolic diseases (CVMD), such as heart disease and type 2 diabetes, represent a major global health burden and exhibit stark ethnic disparities. Current clinical prediction models, even
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indicate increased risk of skin breakdown. The project will involve design and modelling, 3D printing and prototyping, mechanical testing, and evaluation of sensing performance in representative conditions
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physiology and nutrient assimilation. Controlled feeding experiments will trace trophic transfer into zooplankton and higher consumers, generating quantitative coefficients for ecological risk models. Finally