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partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. We invite applications for a Research Fellow to work on mathematical
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such as Geography, Computer Science, Planning, Statistics, Mathematics, Physical Sciences, Social Sciences are likely to be relevant. Or other relevant or equivalent qualifications. They will also have
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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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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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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
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efficiency improvements. 2. Develop mathematical models and simulation tools to represent the dynamic behaviour of onboard power systems under various operating conditions and loads. 3. Utilize optimization
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, Computer Science, Mathematics, or related disciplines with a focus on machine learning, deep learning, or data science. Proficiency in machine learning and computational modelling. Capable of developing and