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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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, access to large-scale IoT deployment data, specialized training in human-computer interaction and AI model development, and direct engagement with communities using the technology. Students will gain
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operational efficiency, and provide valuable data insights for improved system operation. Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships is creating leaders in
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nationality. Experience in working with large aerospace primes would be beneficial including Airbus, Boeing, BAe Systems etc. Ideally the candidate should have a year or more postgraduate research experience in
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to £41,478 per annum. An exciting opportunity for an established researcher or a recently completed PhD researcher with experience in malacology, epidemiology, data mapping and/or schistosomiasis modelling and
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research opportunity focuses on advancing large-scale additive manufacturing using metal wire as feedstock and electric arc as the heat source. The project aims to develop an innovative and efficient method
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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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and Applied Science at Cranfield University, funded by two big aerospace primes. Overview: This exciting PhD opportunity is at the intersection of aerospace engineering and cutting-edge technology. It
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will