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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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, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy
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to completion) or possess equivalent research experience in a relevant computational field such as data science, artificial intelligence, machine learning, computer science or statistics. They will bring strong
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: Experience in software development in topics such as computer vision, audio signal processing, machine learning, deep learning, and/or sensor systems. Experience in collaboration and technology transfer
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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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on basic laparoscopic surgery tasks, using data collected under varying network conditions and applying machine learning and time-series modelling to predict delay. The models will be integrated into a real
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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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We are looking for a creative and enthusiastic Research Assistant for a role focusing on Clinical Machine Learning and Natural Language Processing. You will be working on an exciting project, funded
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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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design. Relevant research focus areas include, but are not limited to, hardware accelerators for machine learning (ML), Artificial Intelligence (AI), neuromorphic computing, and digital signal processing