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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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methods and interdisciplinary approaches and has dedicated awards to offer in these areas. For more information on the LISS DTP core principles and vision please go to our webpage here . Research in
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research frontier in computer vision that combines three critical challenges: class imbalance, recognition of rare and unseen species, and dense labelling of high-resolution imagery. The candidate will
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electronics, enabling spontaneous gaits powered by a single onboard pressure source. The project’s vision is to establish embodied oscillator intelligence, where locomotion arises from the physics of coupled
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Students Project Description The NetZero Futures (NZF) Doctoral Landscape Award is a fully funded EPSRC studentship with the Royal College of Art. The strategic vision of NZF unites RCA-wide Art and Design
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of mission design, manufacturing, operations, and disposal. This PhD project aims to advance sustainable space operations by developing a holistic lifecycle assessment framework and computational tool
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One fully funded, full-time PhD position to work with Alessandro Suglia in the Embodied, Situated, and Grounded Intelligence (ESGI) group at the School of Informatics, University of Edinburgh
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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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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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Deadline: 30 September 2025 A fully funded 3.5 year PhD position is available to work on the project titled “Scalable benchmarking for digital quantum computers based on blind testing