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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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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
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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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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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
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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. dos Santos is an Assistant Professor (Lecturer) in Computer Vision at the University of Sheffield. His research interests include remote sensing image processing, computer vision and machine learning