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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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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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pivotal role in shaping the future of our reward strategy, optimising HR systems and processes, and driving data-led decision-making across the University. About the Role Reporting to the Director of People
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20 Feb 2026 Job Information Organisation/Company Cranfield University Department HR & Development Group Research Field Chemistry Researcher Profile Recognised Researcher (R2) Positions PhD Positions
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the operational life of battery-powered devices and reducing the environmental impact of large-scale deployments. Advancements in this area support the development of sustainable technologies across various
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the following subject areas: Artificial intelligence – both technical understanding and user application aspects Machine Learning Deep Learning and Neural Networks Big Data and Visualisation Cloud
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coefficients. This strategy carries large uncertainty and requires vast amount of expensive and time-consuming experimental data. Worse, sometimes the experimental data is simply inaccessible. The need for cost
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collaboration in time-critical tasks. By integrating foundation models like large language models (LLMs) with physically embodied agents (e.g., drones or vehicles), the research focuses on enabling adaptive
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, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here