797 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in United Kingdom
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modelling experience, an ability to work in a multidisciplinary team and engage confidently with partners. You will have a track record of publishing high impact journal articles, and will have a commitment
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, along with an aptitude for developing new experimental techniques that relate to this exciting research program. To be successful with your application, you will need to demonstrate: An awarded PhD in
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research programme that rethinks how container supply chains (CSCs) are studied in the context of risk, resilience, and sustainability, as part of a prestigious European Research Council (ERC) initiative
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cells in invertebrates. This Leverhulme Trust funded post is available from 1 February 2026 to 31 March 2027. The successful applicant will carry out experimental and computational work related to
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-leading research on scientific understanding of disease mechanisms into innovative therapeutic approaches. Our vision is to play a leading role in discovering new medicines in areas of high unmet need, with
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. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
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machine learning. Supervision will be provided by Prof. Ali Mazaheri, as well as Prof. Fang Gao Smith, and Prof. Helen McGettrick. The successful candidate will have a strong background in psychology
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to $116,094 (PhD Enty Level - $108,156) p.a. plus 17% super Level B - $122,212 to $145,121 p.a. plus 17% super Contribute to sustainable agriculture in Lao PDR Develop expertise in soil nitrogen
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September 2025, with an end date of 15th of June 2026. Your skills and attributes for success: A PhD or last year PhD student in Statistics, Operations Research, Computational Sciences or Economics
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities