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outcome sets. You will either need a PhD (or be nearing completion), in computer science, data science, artificial intelligence/machine learning (AI/ML), health data science, bioinformatics or a related
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this, the postdoctoral researcher will combine machine learning, molecular dynamics simulations and high performance computing (Isambard AI). Applicants must have a PhD in an appropriate area of computational chemistry or
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the School of Computer Science at the University of Sheffield, you will lead the machine learning research on acoustic foundation models for respiratory health. You will work with large-scale real-world
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using polygenic scores, GWAS, Mendelian randomisation, and other statistical and machine-learning methods. The appointed researcher will have research interests aligned with those of our groups and the
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PhD or equivalent relevant experience in computer science, machine learning, medical imaging, biomedical engineering or a related discipline. You will have strong Python/PyTorch skills, experience with
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near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to [email protected] For more information about working at the Department, see www.eng.ox.ac.uk/about/work
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. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role. Candidate requirements Candidates must have expertise in developing computer vision and machine learning
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approaches to their work. About You We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate: Essential Criteria: * Hold or be about to obtain a PhD
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modelling of electrochemical systems, together with experience in machine learning and data-driven modelling. Research Associate: Hold a PhD in Aerospace Engineering, Chemistry, or a related discipline, or
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning