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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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(e.g. physics, engineering, mathematics or computing). Programming experience and mathematical ability in machine learning Experience in delivering research projects including aspects such as study
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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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View All Vacancies Engineering Location: Jubilee Campus Salary: £30,487 to £45,585 per annum (pro rata if applicable) depending on skills and experience (minimum £33966 with relevant PhD). Salary progression beyond this scale is subject to performance. Closing Date: Wednesday 05 June...
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or in extreme environments (e.g., high temperature). They should also be proficient in the use of programming tools (e.g., Matlab and Python) to solve basic numerical and mathematical problems and to
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science, mathematics, physics and sport science. This mix of traditional and modern subjects encourages and inspires future innovators. In the Department of Chemistry, courses are taught in modern
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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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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