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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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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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interactions with potential pathogens. To achieve this objective, we will use the genetically tractable model fungus Zymoseptoria tritici. This fungus also causes Septoria tritici blotch (STB) disease of wheat
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aims to determine the link between lipid remodelling, antimicrobial resistance and intracellular survival using B. cenocepacia as the model organism. The project is supervised by Professor Yin Chen
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. The resulting findings will help understand how the brain works, how we can maintain brain health and what causes brain disease. Methods We will use the fruit-fly Drosophila as a model organism, for its
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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experimental models that bridge laboratory discoveries to clinical applications. The program emphasises interdisciplinary collaboration across orthopaedics, otology, and odontology, integrating basic research
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Project Overview This PhD project is part of an Innovate UK-funded research programme focused on developing a novel ammonia-fueled engine and generator set (genset) demonstrator for harbour and