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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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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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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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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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orchestration, with applications to Finance. This post is advertised as full-time (35 hours per week). We are open to considering requests for hybrid working (on a non-contractual basis) that combine a mix of
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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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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
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and Prof Paul Shearing. The post is funded through a strategic research partnership and is fixed term for up to 2 years. To support the programme, the post holder will be required to carry out research
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and Prof Paul Shearing. The post is funded through a strategic research partnership and is fixed term for up to 2 years. To support the programme, the post holder will be required to carry out research
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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