777 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "UCL" "UCL" "UCL" "UCL" positions at University of Sheffield
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on AI / machine learning approaches, data integration, and evaluation protocols, ensuring alignment with OMAIB’s open research principles and deployment-centric focus. Contribute to the creation, curation
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templates, distributing information and taking notes at meetings including as part of the major incident response. Use and report on health and safety related data in relevant University systems. Maintain
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audiences Essential Application/interview Ability to coordinate information and liaise with a range of stakeholders, including academics, community groups, and partner organisations Essential Application
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and applications, responding to queries, collaborating closely with other Professional Service teams, contributing to the smooth set-up of successful research applications and maintaining data in
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leads and university central teams to ensure deadlines are met and to improve the student experience. This will require an ability to analyse and interpret data and to suggest creative approaches
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information and advice about how to tackle technical tasks, using in-depth knowledge of the University’s infrastructure. Maintain good relationships with contacts in departments to inform them about work being
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-supervise undergraduate research projects, including providing academic guidance on project design, data analysis, interpretation, and research communication. Contribute to quality assurance and enhancement
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the organisation and delivery of a range of marketing activities Develop, manage and maintain processes, tools, databases and spreadsheets to store information in the most appropriate format, adhering to GDPR
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skills, experience of delivering presentations Essential Application/interview Excellent record keeping and data management skills, with appropriate IT experience Essential Application/interview Ability
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Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth)
intermediary data streams that can offer insight into how the component and manufacturing process is performing. Within both of the fields of forging and machining there are numerous industry-ready low-intrusive