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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078v2) Start: Between 1 October 2026 and 1 July 2027
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Location: Central Cambridge We are seeking a highly motivated Research Assistant/Associate in Machine Learning to join an interdisciplinary project at the University of Cambridge focused on machine
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Do you have a strong technical background in Corrosion, Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols
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Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in
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programmes of research, working with synthetic chemists to test our machine learning systems against challenging real world examples. The role holder will lead on a programme of computation and experimental
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Machine Learning Researcher to join a multidisciplinary team of fabrication engineers and AI specialists at the University of Southampton, within the School of Electronics and Computer Science, working in
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To apply for this role please submit the following document(s): CV Supporting Statement (Your Supporting Statement should be no more than 1000 words and should outline how your skills and experience meet the essential and desirable criteria listed above) You can view your progress and...
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symbiosis of cutting-edge AI combined with human support. About the role The Research Scientist in Machine Learning for Wearables will develop predictive deep learning models to assess maternal and partner
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symbiosis of cutting-edge AI combined with human support. About the role The Research Scientist in Machine Learning for Wearables will develop predictive deep learning models to assess maternal and partner
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About the role This project will develop new machine learning techniques to enable efficient production of bespoke products, such as medicines in a high-volume, high-variation manufacturing setting