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Student Development The successful applicant will gain expertise in: Biophysical imaging (AFM, microscopy) Microbiology and molecular biology RNA sequencing and data analysis Interdisciplinary collaboration
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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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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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School/Department Computer Science and Mathematics Liverpool John Moores University (LJMU) is a distinctive, unique institution, rooted in the Liverpool City Region and with a global presence. Our
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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Technology Approval Scheme ) clearance certificate prior to obtaining their visa and to study on this programme. How To Apply For information on how to apply, please see Postgraduate Funding Search | Newcastle
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contacted by email at o.koksal1@westminster.ac.uk . Available Programmes: MPhil/PhD Moving Image and Media Aesthetics MPhil/PhD Expanded Photography Research spans computer science and engineering, including
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commitment to sustainability in materials research, and the ability to contribute to a vibrant, collaborative research environment. Potential PhD programme of study: PhD in Engineering Department: Engineering
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developer and user meetings. You will acquire skills in programming (e.g. Python, FORTRAN, bash) development of quantum chemistry software and stand-alone tools a wide range of computational and quantum
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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