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Field
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familiarity with machine learning and dimension reduction methods are essential. You will demonstrate strong research output, excellent communication skills, the ability to work both independently and
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simulations, machine learning and data analysis, and the opportunity to collaborate with experiments. This position would suit a researcher who has recently completed a PhD in Chemistry, Biochemistry
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swarm learning experiments, and provide technical input to establish local machine learning capabilities. The post offers opportunities to work closely with academic and industry partners, both within and
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Knowledge of machine learning or multi-omics data integration would be highly desirable Essential Application/Interview Deep interest in musculoskeletal research and translational science Essential
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& National Security), underpinned by cross-cutting Fundamental Research. In the Environment & Sustainability Grand Challenge, we will develop machine learning and AI as a transformative technology to benefit
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SMDR. About you To be successful in this role you will be able to demonstrate the following: A PhD in a relevant subject area. Extensive lab experience, preferably with catalytic reaction systems. Prior
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continuous programme improvement. Participate in educational initiatives and activities to enhance student learning outcomes. Requirements: A PhD or a Master’s degree (with significant industry experience) in
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qualifications and experience equivalent to PhD level plus the requisite experience. A2 Specialist theoretical and practical knowledge of Machine Learning and charged particle tracking. Desirable: B1 An up-to-date
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national and international research networks. We are looking for someone with a PhD (or near completion) in a relevant field such as machine learning, biomedical engineering, or neuroscience, with expertise
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departments, and a thriving ecosystem of museums, libraries, and research centres. At Cambridge, our mission is to contribute to society through world-class education, learning, and research. With a deep