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exciting opportunity to contribute to a major regional innovation programme while working as part of a dynamic and supportive team within the Sir Peter Rigby Digital Futures Institute. This role will be
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applications from candidates with lived experience (either personal or within your family) of the areas covered within our research remit including common mental health difficulties, substance use issues
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applications (e.g. natural language processing, multivariate time-series data), to develop systems that improve the efficacy of machine learning-based technologies for healthcare applications. You must hold a
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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applicants will work as part of an analytic team, including Prof Murphy (KCL), Dr Charlotte Pretzsch (KCL), and Prof Christine Ecker (Frankfurt). Beyond this core analysis team, the candidates will have the
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leading academic and industry experts, and an active academic/industry exchange program that aims to accelerate career development for the postholder employed on the project. About you The postholder should
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records of experimental work and findings. You will take initiatives in the planning of research; conduct and plan own scientific work with appropriate supervision and present findings to colleagues within
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the Department of Engineering Science at the University of Oxford. The post is funded by the Oxford Martin Programme on Circular Battery Economies. It is fixed term up to December 2027. You will undertake
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Applicable. Working at Durham University A globally outstanding centre of teaching and research excellence, a warm and friendly place to work, a unique and historic setting – Durham is a university like no
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project will involve both remote-sensing and field-based observations and data collection. It will provide outputs to the World Bank CAWEP (Central Asia Water Energy Power) programme to aid the design