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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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scattering with computer modelling such as molecular dynamics simulations and AI-assisted data mining. The new technical capabilities will help bridge the current gap in biocide development, i.e., to link
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and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and
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4 Nov 2025 Job Information Organisation/Company The University of Manchester Department Computer Science Research Field Computer science » Computer systems Researcher Profile First Stage Researcher
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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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science, engineering, mathematics, or related subject) Proficiency in English (both oral and written) Essential to have strong foundations in computer systems through degree courses or equivalent work experience
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Acceptable first degree - Physics, Mathematics, Natural Sciences, Engineering, Computer Sciences. The standard minimum entry requirement is 2:1. Mode of study: Full-time Start date: 1st October 2026 Funding
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biological materials. The development of novel computer-vision-based techniques for contactless detection, quantification, and prevention of sport injury. The development of robotic humanoid simulator and
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science, to deliver molecular mechanics force fields that bridge the gap to quantum mechanical accuracy for biological modelling and computer-aided drug discovery. They will develop electrostatic embedding
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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy