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to enable discoveries that improve people’s lives. Its 20-year vision is for large-scale data and advanced analytics to benefit every patient interaction, clinical trial, and biomedical discovery and to
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models. PIML can learn from small amounts of data and are more immune to hallucinations than conventional AI, making them exceptionally suited for biomedical applications. Research Environment You will
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& Medical Sciences and have access to invaluable clinical datasets provided by St George's Hospital. With the support of leading biomedical researchers with strong ties to radiology, your work holds the
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materials and develop new design methods, for functional 4D-printed devices with either fast self-resetting responses or complex multi-scale shape changes, applicable to biomedical, micromechanical
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honours degree (or equivalent) in the physical or biomedical sciences. We welcome candidates who are passionate about science, eager to work across disciplinary boundaries, and motivated to tackle real
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a growing field, with many applications in biomedical devices, electronics, and autonomous machines. Actuators to drive these robots utilise electronic, chemical, pressure, magnetic, or thermal
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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, interpretable diagnostics across modalities Improving predictive accuracy in clinical decision support tools Contributing to equitable and generalizable AI for biomedical applications Candidate Profile: We
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allows them to be processed into various forms such as membranes, sponges, gels, scaffolds, and microparticles, making them suitable for a range of biomedical and aquaculture applications (e.g. wound
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robotics, and materials science. Project description: 3D-printing of soft robotics is a growing field, with many applications in biomedical devices, electronics, and autonomous machines. Actuators to drive