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research in the field of AI for healthcare autonomous systems. Activities on non-healthcare systems could occasionally be requested. • Undertake research from algorithm development to real time
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unique combined system using an optimised AF scanning procedure that integrates Raman measurements to analyse lymph node biopsies within 10 minutes and machine learning algorithms to deliver quantitative
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and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre at the Nottingham Breast
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group; a multi-sectoral team of researchers, data analysts and project specialists working on producing data on population distributions and characteristics at high spatial resolution. We are looking
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of distributed optical sensing techniques developed in ECSTATIC, such as novel distributed acoustic sensing (DAS), on trackside telecom fibres, for the condition monitoring of railway infrastructure, focusing
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for the application and validation of distributed optical sensing techniques developed in ECSTATIC, such as novel distributed acoustic sensing (DAS), on trackside telecom fibres, for the condition monitoring of railway
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. Awards will be finalised by July 2026. Supported Areas for START - Engineering Energy Resilience: Power electronic converters for distributed energy resources and microgrids; High voltage DC transmission
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strengths in global health research, bringing cohesion to the distributed community of Oxford scholars working in this area, transforming the visibility of Oxford Global Health externally, and championing key
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research, bringing cohesion to the distributed community of Oxford scholars working in this area, transforming the visibility of Oxford Global Health externally, and championing key challenge areas
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distributions and characteristics at high spatial resolution. We are looking for an experienced, self-motivated, and dynamic researcher with a track-record working and publishing in the field of evidence-informed