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We are looking for a Postdoctoral Research Associate reporting to the Principal Investigator Prof Yee-Whye Teh, they will be a member of the Oxford Computational Statistics and Machine Learning
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specialist areas such as programming for finance and machine learning. You bring experience delivering high‑quality learning both online and in person, using digital tools and real‑world applications to bring
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, digital health outcomes and wearable technology advanced analytics. This post will involve collecting wearable technology data, implementing novel machine learning advanced analytics for processing and
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
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models (mathematical + software) of existing grid operational practices Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems Developing machine
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information/data from one structure can be used to manage similar structures from the same population. New machine learning, sensing and digital twin technologies will be developed with the aim of driving new
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lifecycle. You’ll have a particular focus on employee relations, learning and development, and using data to drive continuous improvement. Your support will empower managers to lead confidently and create
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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(Research Assistant) or PhD degree (Research Associate) in computer science or a related area or equivalent experience. Familiarity with standard machine learning libraries/data analysis, specifically as
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Development and Validation of a Multimodal Wearable Headband for Objective Bruxism Monitoring Using Machine Learning (S3.5-DEN-Boissonade)