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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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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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using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new
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This project is sponsored by EPSRC, Cranfield University and a consortium led by ETN global. ESPRC will provide stipend (£22,000 per annum) and cover UK fees. The consortium will provide supervision
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academia and industry. Uncover and quantify critical degradation mechanisms to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through
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to inform the design of next-generation fusion materials. Translate sophisticated scientific data into impactful insights through clear communication to diverse audiences, including industry stakeholders and
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diverse and under-represented backgrounds and can offer flexible working arrangements. Funding Sponsored by EPSRC and three industrial partners, this studentship will cover: PhD fees; research equipment and
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per month). The Centre for Materials at Cranfield will cover the costs associated with manufacturing, testing, and facility access.
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, intelligent monitoring systems and predictive technologies have become essential competitive advantages. This project sits at the intersection of data science, engineering, and design innovation, addressing
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directly addresses one of the most pressing challenges in modern information security: the malicious use of synthetic media. The relevance is acute, as deepfakes and AI-powered phishing campaigns are being