111 data-"https:" "https:" "https:" "https:" "https:" "https:" "P" positions at University of Greenwich in United Kingdom
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Closing Date: Sunday 19 April 2026 Interview Date: To be confirmed Reference: 5297-E Our Information and Library Services Directorate delivers award winning IT services and support, library, academic and
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Financial Management Information - Management Accounts Location: Avery Hill or Greenwich Campuses Salary: £58,225 to £67,468 plus £5476 London weighting per annum Contract Type: Fixed Term - 12
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Operations Support Analyst to join our People Directorate on a fixed-term basis until 31 July 2026. This is an excellent opportunity for someone with strong analytical skills who is passionate about data
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aspirations), sharing information, and providing advice on funding sources including focussed advice/support on EU opportunities. The team will actively support bid preparation activities and take the lead on
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opportunity. Please contact Mary Ivers, Interim Associate Director, External Engagement at m.b.ivers@greenwich.ac.uk for more information. Eligibility Criteria Priority for the scholarships will be given
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the implementation of functional enhancements to the HPC. The role sits within the Platforms & Research Systems Team, in the Information & Library Services Directorate (ILS), complementing the existing operations
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the goal to achieve better decision-making across the institution from the analysis, advice and insight we provide, to the trusted relationships we build with stakeholders and the data quality, governance
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@greenwich.ac.uk Further details of the fantastic benefits and what we offer can be found here: Our benefits - what we offer | Jobs and careers | University of Greenwich and information on the university’s working
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to all stakeholders. Experience of undertaking large scale data gathering and information retrieval with the ability to balance competing demands and manage customer expectations. Good attention to detail
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to learn from. Augment Data with Generative AI - You will use physics-constrained generative models (Diffusion models/GANs) to create a diverse library of synthetic microstructures, improving model