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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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economy and de-centralised manufacturing. To enhance process efficiency (clean) by researching sensors and big data for factory management, product service systems, factory modelling and artificial
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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CFD-FEA Combined Thermal-Fluid-Mechanical Modelling for Defect Control in Additive Manufacturing PhD
MSc and PhD research, and its rolling technology development programme on large-scale additive manufacturing. This project will have close links to EPSRC research programme of Sustainable Additive
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This PhD project will investigate the recent field of study of Causal Machine Learning, which aims to modify and augment Machine Learning by using Causal Analysis techniques as a way to solve its
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Though portable IR sensors exist in the market, there is no system currently in place to use these sensors to perform active thermography inspection. This PhD project aims to develop a IoT based
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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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Methane is a major greenhouse gas and so contributes to climate change. Cranfield and SLB are offering an iCASE PhD studentship, developing modelling methods to couple with observations of methane