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in geomaterials. A particular focus will be on the soil micro-structure evolution and how this links to macro-scale behaviour, using advanced laboratory tests, imaging techniques, and computer
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for high-energy X-rays, thermal instability, or issues related to toxicity and cost. As imaging demands increase—particularly in fields like high-throughput synchrotron science, medical diagnostics, and
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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candidate with experience in cell culture, stem cells, molecular biology, immunohistochemistry, biochemistry, protein purification and/or confocal imaging. Experience in using human tissue is advantageous. A
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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skills. Main duties will include: conduct tissue-mechanical and imaging experiments using early avian embryos; acquire and process data; prepare reagents and samples; optimise protocols; program and debug
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Alex Leung (Mechanical Engineering at UCL) will also collaborate. he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect
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settings. The project will be supervised by experts in DIC (Hari Arora), surgery (Iain Whitaker) and wider biomaterials imaging research at Swansea University (Richard Johnston), building on decades
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Modern cyber-physical systems (CPS), such as UAVs, next-generation fighter aircraft, and command-and-control (C2) platforms, integrate digital computation with physical processes to make mission