609 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" Fellowship positions in United Kingdom
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/non-clinical partners. Your research strategy will have a strong emphasis on developing ex vivo models to mimic in situ normal breast facilitating compound screening for translational prevention trials
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practices. We will support you to develop your independence as a researcher through training and mentoring, whilst also providing opportunities to develop your grant writing skills. Please note that this post
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research-intensive university in North England, UK. Our university’s 2030 strategy is to develop and shape a fairer, brighter and carbon-neutral future. Our mission is to advance education, scholarship
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this role you need to be able to show a body of research outputs and the level of thought leadership and development towards independence required to be competitive as a future research leader. In this role
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. Interface 2021, 13, 35506), the Soft Matter Engineering group is leading a European research consortium in developing sustainable antimicrobial surface coating (https://www.supreme-project.eu ). Role Summary
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at the forefront of developing and delivering novel therapies, with opportunities to engage in pioneering clinical research, contribute to service development, and gain hands-on experience in a rapidly
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wider consortium project: The development of efficient microorganism-driven processes that generate bioactive natural products is of immense biotechnological, biomedical, environmental and economic
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Fixed term – 48 months Full time, 37 hours (37 hours per role) Closing date: 27/08/25 at 23:30 Are you passionate about supporting the development of resilient and well-connected communities? Do you
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We are looking for a motivated and talented postdoctoral-level researcher with experience in executable modelling to join a cutting-edge project developing Digital Twins for rare diseases. This is a
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. The project will define new near miss and severe morbidity definitions allowing us to identify electronically when significant events happen. We will then develop a large multi-centre maternity routine dataset