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Field
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
and reasoning techniques to support predictive maintenance and asset health monitoring •Design feedback mechanisms that deliver interpretable insights (e.g. alerts, recommendations, confidence scores
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randomised controlled trial (RCT) using a waiting list design. Fifty school pupils aged approximately 11 to 18 with non-anorexic eating disorders will be recruited from schools in the Midlands. Participants
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The project focusses on the healthcare and sports application development related to super-smart textiles and other wearable electronic technologies. This incorporates the user-centric design considerations
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Engineering, or equivalent work experience. Degree or equivalent in appropriate discipline Certificates or verified experience Experience Knowledge of control systems design. Clear communicator with good
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into their structure. However, we don’t yet know how to design these complex printed parts and we are still developing new leading-edge materials to grant them new capabilities. Advantages: In this PhD studentship, you
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collaborative project that spans multiple continents. You will contribute to the development of new chronobiological analytics on existing data, design experiments to collect novel chronobiological data, engage
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demonstrated experience in designing and implementing research projects in mental health or related areas. Possess excellent communication and interpersonal skills for effective collaboration. Have highly
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sectors like aerospace, healthcare, and manufacturing. The convergence of AI with fault-tolerant design principles is transforming traditional maintenance paradigms, leading to more robust and intelligent
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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transfer and laser-matter interactions. The “design-manufacture-inspect-model-test” approach of this project will equip the successful PhD candidate with a wide range of valuable and transferable skills