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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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performance degradations and unwarranted system failures can occur. There is certain physical information known a priori in such aerospace platform operations. The main research hypothesis to be tested in
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the economic viability of these sensors to enhance real-time data collection and improve monitoring practices, and the social factors that influence their uptake. This studentship offers a unique opportunity to
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methods in the past. A piece of comprehensive computer software, Pythia with the corresponding capabilities have been developed and tested successfully in several industrial applications. The software can
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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fitting for reduced order electrochemical models. Early detection of thermal anomalies in battery packs. Physics-based models and state of health estimation in lithium-sulfur batteries. Collecting data and
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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operability, including prediction of critical phenomena such as water hammer. The methodology will be verified against industrial data regarding performance and operation. You’ll join a multidisciplinary team
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numberSATM533 Entry requirements Applicants must have a B.Sc. in electronic / information engineering or computer science and must either have or close to having a Master’s degree (must be completed by the time
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utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images