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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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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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existing data analytics tools will help deploy these technologies in the industry context without the need for big datasets. Predictive Maintenance (PdM) is one of the maintenance strategies that has
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specific courses aimed at the different phases of a PhD. For example, the programme includes aspects such as research methods, technical report writing, presentation skills, data management, leadership
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and manufacturing methods. The Centre's contributions to industry are demonstrated through its extensive MSc and PhD research initiatives and its ongoing technology development programs in large-scale
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
predictive and explainable digital twins. The core challenge this PhD will tackle is how to help digital twins make sense of complex, messy maintenance data and turn it into clear, useful insights
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This fully-funded PhD research opportunity, supported by EPRSC Doctoral Landscape Awards (DLA) and Cranfield University offers a bursary of £22,000 per annum, covering full tuition fees. This PhD
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This PhD opportunity at Cranfield University invites ambitious candidates to explore the frontier of energy-efficient intelligent systems by embedding AI into low-power, long-life hardware platforms
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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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University. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of gas turbine performance, diagnostics and prognostics