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duration of 3 years starting 2 September 2024 as part of the UKRI Future Leaders Fellowship: “Using Mathematics to Disentangle the Role of Synaptic Communication.” This interdisciplinary fellowship aims
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: 2704324PJ At the School of Mathematical Sciences, the Operational Research group is seeking to appoint a full time Research Fellow for a fixed period of 6 months. There may be a possibility of extension
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mathematical modelling, simulation techniques, and optimization algorithms, coupled with a strong understanding of energy systems dynamics and renewable energy technologies. The appointee will be expected
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excellence in public and global health research, education and translation of knowledge into policy and practice. This is an exciting opportunity for a mathematics / engineering / computer science or highly
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. This is an exciting opportunity for a mathematics
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expertise in economics, engineering, governance and applied mathematics. The team will work interactively and dynamically, in order to quickly develop important new insights, drawing upon extensive research
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to explainability, based on attention mapping, as well as develop novel mathematical tools aimed at improving transparency of clinical AI. The development of these 3 lines of research, will be facilitated by bench
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Job id: 089405. Salary: £62,696 - £64,421 per annum, including London Weighting Allowance. Posted: 10 May 2024. Closing date: 14 May 2024. Business unit: Faculty of Life Sciences & Medicine. Department: Perinatal Imaging & Health. Contact details:Professor Mary Rutherford. ...
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Location: Radcliffe Observatory Quarter, Woodstock Road, Oxford About the Blavatnik School of Government Our vision is of a world better led, a world better served and a world better governed. We are a global school committed to improving the quality of government and public policymaking...
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clinical AI. Explainability – the postholder will apply standard approaches to explainability, based on attention mapping, as well as develop novel mathematical tools aimed at improving transparency