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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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, seminars, publishing support, and conference participation, as well as opportunities to engage with practitioners through thought-leadership activity, developing a powerful mix of research, analytical, and
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degree or equivalent in a related discipline. This project would suit individuals with academic or industrial experience in electronics, electrical engineering, systems engineering, or AI/data analytics
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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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subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal
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, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems
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relevant discipline/subject area. A minimum of English language proficiency (IELTS overall minimum score of 6.5). Also, the candidate is expected to: Have excellent analytical, reporting and communication
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simulation, data analytics and experimental validation, alongside applied mycology, and food safety risk modelling. Transferable skills will include collaborative systems thinking, stakeholder engagement
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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