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PhD Studentship: Rolls-Royce sponsored PhD scholarship – Intelligent Mechatronics and Robotics Systems Engineering Rolls-Royce University Technology Centre (UTC) in Manufacturing and On-Wing Technology, The University of Nottingham. Applicants are invited to undertake a three-year PhD programme...
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PhD Studentship: Rolls-Royce Sponsored PhD Scholarship – Micromechanics and In-Depth Materials Analysis of Advanced Aerospace Materials Upon the Manufacturing Process Engineering Applications
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dealing with investigations on the development of bespoke high-tech laser beam processing methods for surface treatment and repair of aero-engine components. The project will deal with the study of a new
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing
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various chemical additives, dyestuff, pharmaceuticals as well as the precursor of photo initiator. However, in the conventional process, the synthesis of above-mentioned C-C coupling products focuses
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research across three core areas of research competence: Circular Economy, Ecological Digital Citizenship, and Robotics & AI. These areas align with the EPSRC Discovery priority of Digital Futures, and the
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, and interactive digital platform. This platform will support the Living Lab’s mission to foster collaboration and knowledge-sharing around health technology development and evaluation. The postholder
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Challenges & Transformations Health, Wellbeing & Society Sociotechnical Futures & Digital Methods The SWDTP has a steer of three studentships to award in both Advanced Quantitative Methods and Data Skills. As
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap