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United Kingdom Application Deadline 9 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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detection models, with a focus on achieving generalisable multimodal understanding in zero-shot settings. About You The successful candidate must have a PhD (or equivalent) in the field of computer vision or
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United Kingdom Application Deadline 9 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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London, with a team of investigators covering AI, computer vision, robotics, and medical imaging. You will join a dynamic and successful team with access to both cutting-edge computer power and advanced
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oversee data collection, management and analysis, and lead the dissemination of findings to academic and lay audiences. You will work closely with families, NHS Trusts and charity partners, and contribute
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developmental and experimental assessments across the UK. The successful applicant will oversee data collection, management and analysis, and lead the dissemination of findings to academic and lay audiences. You
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their FTE). Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value of benefits provided by Heriot-Watt University Role Description We are seeking
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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of agentic behaviour and publishing high-impact research. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling