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in computer vision and intelligent transportation. Experience with tools such as MATLAB, Python or machine learning frameworks is highly desirable. Supervisor: Dr Ning Zhao (N.Zhao@bham.ac.uk
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wind turbines are pivotal in driving the global transition to renewable energy; however, maintaining these critical assets poses substantial challenges. Advances in computer vision and deep learning
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PhD Studentship: Secure By Design Datasets – Distillation and Privatisation for Future Collaboration
and Signal Processing at the University of Surrey. This is the largest such UK institute and is ranked 1st for Computer vision research in the UK and 3rd in Europe (csrankings.org). The supervisory team
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Background Adverse events and preventable failures in healthcare services represent a key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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you to participate in any element of the recruitment process you can contact d.costanzo@sheffield.ac.uk Our vision and strategic plan We are the University of Sheffield. This is our vision
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or second-class honours degree in engineering, physics, mathematics, computer vision, or a related field. Interest or experience in computational modelling or coding—beneficial but not required. A
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which
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of medical processes Background Adverse events and preventable failures in healthcare services represent a key area of patient safety that can be improved with the use of computer vision approaches to system
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University Technology Centre (UTC) in Manufacturing and On-Wing Technology, The University of Nottingham. Applicants are invited to undertake a 3-year PhD program in partnership with the UK Atomic Energy