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both the fundamental physics of electromagnetic materials and practical applications in 6G communications. The PhD is 4 years and funded by DSTL (Defence Science and Technology Laboratory), and you will
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highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
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At the Faculty of Engineering and Science, Department of Materials and Production a position as PhD stipend in Muscle Neuromechanics and Ultrasound Imaging, within the doctoral programme Materials
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Testing (NDT) – a crucial technique used in industries to assess materials, components, and structures without causing damage. This PhD project focuses on advancing Ultrasound Testing and X-ray Computed
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programmes in the first three years, of which will be followed by committing to an industrial placement in the fourth year of this doctoral programme. Supervisors: Entry requirements: Essential: A first or
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the harmful effects of deepfakes on ordinary people. You will draw on methods from psychology, linguistics, data science, and computer science, such as behavioural analytics, neuroimaging, face and voice
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stabilization. We will take advantage of a unique gradient in precipitation found on common parent material in Spain. We will study above- and belowground litter decomposition and SOM stabilization by applying
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precision machining, tools are prematurely discarded to avoid the risk of costly non-conformities. This creates a large loss of value of strategically valuable raw materials and waste of energy related
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for the production, storage, and utilization of hydrogen as an energy source. Central to hydrogen-based technologies are the functional materials that facilitate them. Recently, our research teams have identified a
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samples, by the development of advanced computational multiphysics models of stress corrosion cracking and coupling these with process-microstructure models (being developed within MicroAM project). Main