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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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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 PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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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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This PhD project aims to address one of the key challenges in the manufacturing industry, the increase in productivity by utilizing the equipment with its optimum performance and without any
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
developed a dataset by conducting high-velocity impact experiments on CFRP specimens using controlled testing setups. The multimodal dataset is to be processed using X-ray CT scans, SEM imaging, and
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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A funded PhD studentship is available within the Autonomous and Cyber Physical Systems Centre at Cranfield University, Bedfordshire, UK. As aerospace platforms go through their service life, gradual
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, and flexible working arrangements ideal for computational and field-integrated PhD research. Methodology You will develop a process-based, spatially explicit population model for European amphibians
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits