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This exciting fully funded PhD, with an enhanced stipend of £25,726 pa, is sponsored by Anglian Water, Thames Water, Yorkshire Water, Northumbrian Water and EPSRC. The research will address
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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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students and a tax-free bursary of £25,183) PhD, sponsored by the Manchester Airports Group (MAG), aims to support airports in identifying clear requirements and phasing considerations when planning
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infrastructure. This project sits at the interface of environmental engineering, water quality management, and sustainable infrastructure. This PhD project will explore how ICWs can be strengthened through design
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Cranfield University is excited to invite applications for a PhD studentship focused on developing and validating innovative origami-paper eDNA sensors with community scientists for the rapid
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We are seeking a highly motivated candidate to undertake a PhD program titled "3D Temperature Field Reconstruction from Local Temperature Monitoring in Directed Energy Deposition." This exciting
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Cranfield is an exclusively postgraduate university that is a global leader for transformational research and education in technology and management. Research Excellence Framework 2014 (REF) has recognised 81% of Cranfield’s research as world leading or internationally excellent in its quality....
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This exciting fully funded PhD, with an enhanced stipend of £25,726 per annum (with tuition fees covered), is sponsored by Anglian Water and EPSRC. It directly tackles one of the central challenges
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This fully funded PhD studentship, supported by the EPSRC Doctoral Landscape Awards (DLA) and industrial partner Crover Ltd, offers home fees (£5,238 p.a.) and a £23,000 annual tax-free stipend
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap