214 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" positions at University of Nottingham
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. For information about the School of Mathematical Sciences and active research themes see: http://www.nottingham.ac.uk/mathematics/index.aspx . If you are interested in this position, please click Apply Now and
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- dave.butler@nottingham.ac.uk. Please note that applications sent to this e-mail will not be considered. All of our vacancies are available to view at: https://www.nottingham.ac.uk/jobs/home.aspx Our university
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permanent post for 52 weeks of the year plus all of the tools you need to do a great job. · Uniform provided. All of our vacancies are available to view at: https://www.nottingham.ac.uk/jobs/home.aspx
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of our vacancies are available to view at: https://www.nottingham.ac.uk/jobs/home.aspx Informal enquiries may be addressed to dave.butler@nottingham.ac.uk . Please note, applications sent directly to this
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to curriculum development, ensuring innovative approaches to learning, teaching, and assessment that reflect international best practice. Lead and support major funding bids, develop partnerships with industry
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extended by mutual agreement. Applicants are invited to submit their applications via the application link https://jobs.nottingham.edu.cn/job/184369/ by 23:59 Beijing Time, 23 February 2026, which should
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the role profile. Refer to our candidate guidance on writing an application and the use of AI: https://www.nottingham.ac.uk/jobs/candidate-guidance/writing-your-application.aspx Hours of work are full-time
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All our vacancies are available to view at: https://www.nottingham.ac.uk/jobs/home.aspx Our university is a supportive, inclusive, caring and positive community. We welcome those of different cultures
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foundation in either machine learning or mathematical/computational neuroscience, demonstrable programming experience (Python/PyTorch), and the curiosity to work across disciplinary boundaries. A background in
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. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model. The EngD