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Early and accurate cancer detection remains a critical global healthcare challenge, with profound implications for patient outcomes and treatment strategies. While Time-of-Flight Positron Emission
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Early and accurate cancer detection is a major global healthcare challenge, with significant implications for patient outcomes and treatment strategies. Time-of-Flight Positron Emission Tomography
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applications for a PhD studentship focused on developing and validating innovative origami-paper eDNA sensors with community scientists for the rapid detection of chemical and microbial contaminants in rivers
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detection of chemical and microbial contaminants in rivers. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme, which is supporting new research
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for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated
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innovation and find diverse applications across industries such as aerospace, energy, and automotive. Among its various techniques, wire-arc directed energy deposition (WA-DED) stands out as a highly promising
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of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong
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for efficient solar energy harvesting. This project will deliver novel methods for modelling and controlling LSS structural dynamics in the extreme orbital environment. The objectives are as follows: 1. Undertake
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
techniques (e.g., ultrasonic C-scan, X-ray CT, thermography) rely heavily on expert interpretation, are time-consuming, and often fail to detect subsurface or latent damage accurately. Advances in artificial
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By