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Field
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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mechanisms, namely the remodelling of membrane lipids. Lipid remodelling is a process whereby bacteria selectively modify their membrane lipid composition in response to a particular environmental stimulus
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods
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to the interior of the cell. GPCRs are involved in multiple physiological process including cell growth, neurotransmission, metabolism & immune response; they can misfunction in disease and consequently have served
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as early indicators of anthropogenic and climate-driven change. However, limited understanding of the processes shaping species’ biogeographic distributions constrains our ability to predict ecological
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of the underlying the physical processes leading to predictability and forecast skill of North Atlantic cyclones. The project will also explore statistical post-processing methods to see if raw forecast skill can be
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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and
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for Real-World Optimisation and AI Applications Brain-Computer Interfaces & their Applications Computational Neuroscience: Reinforcement Learning and Microzones in the Cerebellum Explainable Generative
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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
developed a dataset by conducting high-velocity impact experiments on CFRP specimens using controlled testing setups. The multimodal dataset is to be processed using X-ray CT scans, SEM imaging, and