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Field
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, you will work at the intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types
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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role. Candidate requirements Candidates must have expertise in developing computer vision and machine learning
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automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position We have a vacancy for a PhD candidate in machine learning at the Department
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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or AI/machine learning/NeuroAI. For candidates with a neuroimaging background, this may include experience with data acquisition, preprocessing, and/or analysis; experience with fMRI is preferred, but
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appointment. Job duties Mentoring new PhD students in numerical computational projects. Conducting comprehensive literature reviews in machine learning for subsurface flow and well performance and supporting