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We are seeking a full-time Postdoctoral Research Associate in Machine Learning for Grid-Edge Flexibility to join the Power Systems Architecture Lab within the Department of Engineering Science
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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About the Role A fantastic opportunity has arisen for a Senior Research Fellow to join the Power Electronics, Machines and Drives Research Institute (PEMC) at the University of Nottingham and become
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projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
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should have, or be close to completing, a PhD in Statistics, Probability, Machine Learning or a related discipline. You will work directly with the investigators to undertake and support research necessary
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science, medical statistics or machine learning methods Advanced knowledge of electronic healthcare records and their use in development and validation of risk prediction models Knowledge in application
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of degradation pathways and shelf-life prediction. The aim of project is the safe integration of machine learning methods within the biopharmaceutical development process. This project offers an opportunity to be
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» Computer engineering Technology » Computer technology Technology » Future technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country United Kingdom Application Deadline 28 Mar
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) Programming (e.g., real-time audio, games engine, machine learning toolboxes, git) Acoustics (e.g., sound properties, room modes, reverberation, HRTFs) Participatory research (e.g., formal listening tests