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) advances in imaging techniques that fuel a more detailed understanding of the brain, 2) tools from artificial intelligence that enable building better computer simulations of the brain. The lab will leverage
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of computer science and engineering. Research areas incorporate digital signal and image processing, sensor network, Internet of Things, healthcare as application area, multimedia, image and video processing, cyber
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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power consumption. Many emerging biomedical devices, such as non-invasive and indwelling systems, require reliable operation and continuous feedback on biochemical conditions at the device–tissue
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benefit from the extensive and broad expertise in AI and biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical
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expertise in AI and biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a multidisciplinary team at KCL, UCL and with clinicians
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strong upper second-class undergraduate degree in Chemistry, Physics, or a related discipline such as Mathematics or Computer Science, are encouraged to apply. The candidate is expected to have a strong