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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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proposal. This PhD will evaluate the efficacy and suitability of digital image collection and analysis for beach litter characterisation on heavily-littered coastlines, in partnership with community groups
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operators in a cycle of designing bespoke, inflexible models. Large Language Models (LLMs) represent a paradigm shift, offering a path to a more sustainable and intelligent approach. Their emergent
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: environmental monitoring, AI, computer vision or multispectral imaging. Entry Requirements At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5
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on health Physics-informed image registration approaches to infer injury mechanisms from longitudinal image data. Experimental characterization of the mechanical properties and damage behaviour in soft
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millions of images of wildlife worldwide, offering unprecedented insights into ecosystems. However, analysing these datasets is a major challenge: AI systems excel at detecting common species but often fail
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). The successful candidate will design and prototype intelligent textile/wearable systems capable of sensing, communication, and stimulation. The project will integrate wearable electronics, advanced manufacturing
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results from human decision-making to inform the design of this new paradigm, and feed the results of the latter back into human decision-making to help make it more explainable. The PhD student will: (1
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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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-driven shifts in species distributions. Currently, barnacles and other species are manually counted from over 3,000 images each year, which is time-consuming and prone to human error. This project will