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Field
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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This is a fully funded PhD (fees and bursary) in experimental icing research. Fundamental understanding of droplet impact dynamics is integral to icing. The overall aim of this PhD is to use optical
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This research focuses on a critical challenge: understanding and mitigating hydrogen pre-ignition risks caused by lubricating oils under demanding engine conditions. Hydrogen is a clean, zero-carbon fuel, but its high reactivity makes it vulnerable to pre-ignition. The presence of lubricating...
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departments and research centres in the School of Policy and Global Affairs, offering potential for interdisciplinary research in politics, sociology, or criminology. About the programme Upon joining our PhD
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(IoT) device ecosystem. Despite the technology’s potential, however, flexible electronics face numerous technology challenges. This PhD project aims to tackle one of the most critical technology
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Award Summary 100% home fees covered, and a minimum tax-free annual living allowance of £19,237 (2024/25 UKRI rate). Additional project costs will also be provided. Overview This PhD studentship
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. Funding This is a self-funded PhD. Find out more about fees. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and
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, Aerospace and Civil Engineering at the University of Sheffield, and embark on a transformative PhD project funded by John Crane Ltd, a global leader in engineering technology. What’s the Project About
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with a research component, or those with strong research skills, including qualitative research, and knowledge of the social environment of healthcare. In either case, the PhD will benefit from the high
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
aerospace environments. The objectives of the PhD are: •Extract structured engineering knowledge from unstructured maintenance data using LLMs, and represent it using ontologies and knowledge graphs •Develop