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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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and industry coordination. The postdoc will contribute to empirical research, scholarly publications, and the design of human-AI collaboration models. Work will be based at the University of Nottingham
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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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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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during pandemics. Populating these instances with real-world data we would then develop novel algorithms to solve them. The selected candidate would disseminate their research by publishing in top-tiered
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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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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