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biosensor, transforming an existing prototype technology into a deployable environmental monitoring solution. Building on recent innovation at Exeter, this PhD will translate a novel discrete gape-sensor unit
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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(Academic Technology Approval Scheme ) clearance certificate prior to obtaining their visa and to study on this programme. How To Apply Please read and complete this document as your Personal statement, and
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by developing AI methods that improve recognition of rare species while providing reliable measures of uncertainty. Using state-of-the-art computer vision approaches — vision transformers, self
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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and
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Microfluidics and lateral flow assay engineering Translational diagnostics and AMR-focused assay development Digital image analysis and Python-based data processing The project includes opportunities
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Programme (NSIP). The position is full-time and fixed-term until 31 March 2027. This interdisciplinary project is conducted in collaboration with the Department of Plant Sciences and other industry partners
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well as reliability in extracting target features. The project covers UK tuition fees and the standard UKRI PhD stipend and it is co-funded by the Quantum Hub in Sensing, Imaging and Timing (QuSIT) and BAE Systems