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this testbed available to users for testing hardware and applications. NPL will lead on the testing and security evaluation of the testbed and collaborate widely on the technology development. The student’s
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multidisciplinary research team within the School of Electrical, Electronic and Mechanical Engineering. SMG is known for its work in computational mechanics, fatigue, fracture, and advanced materials modeling
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, engineering (e.g.: mechanical, electrical, electronic, materials) or a related subject. A relevant master’s degree and/or experience in the use of wearable technology will be an advantage but not essential
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control. Joining the leading researchers in the Centre for Engineering Research at the University of Hertfordshire, collaborating with our industrial partners and becoming a member of the vibrant doctoral
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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the properties of post-consumer recyclates. The project will apply cutting-edge data science techniques, including principal component analysis and machine learning, to handle multivariate datasets and enhance
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A position is available for a Postdoctoral Research Associate (Postdoc) in the forthcoming Aspirational Computing Lab in the Department of Computer Science and Technology at the University
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for understanding natural magmatic processes on earth & other planetary bodies. Neutron diffraction is a powerful technique for studying the atomic scale structure of these materials, but the current technology to
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interdisciplinary team working on the development of cross-platform alignment technologies that integrate material science, process engineering and sustainability analysis to deliver scalable solutions for circular
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health