594 computer-programmer-"https:"-"U"-"UCL" "https:" "https:" "https:" "https:" "https:" "https:" "P" positions at University of Oxford
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executive education programmes through our trading subsidiary, OSBS Ltd, equipping leaders from around the world with the skills to create meaningful impact. Our Human Resources Team is central to
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research collaboration focused on economic modelling and return-on-investment (ROI) analysis for programmes improving outcomes among adolescent girls and young women (AGYW) across Africa. This post is part
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. High level of computer literacy, including MS Office and web-based systems, including Excel, Word, Ms Teams and Outlook is essential for this role. You will have significant administrative experience in
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), which will inform the University’s plan of action in this area. We are looking for two individuals who have sufficient knowledge in this area of work, and are able to manage their own research and have
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the Student Assessments section. The successful candidates will work with the team to plan and support the running of student assessments across the University. The role is varied, and the postholder can expect
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possess sufficient specialist knowledge in the discipline to work within established research programmes and experience running mesocosm experiments in aquatic environments. Excellent communication skills
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the range of benefits we provide, including: • Flexible Hybrid Working: A work setup that combines office and remote work. • Excellent Contributory Pension Scheme: We help you plan for your future
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or articles under revision in premier management journals (e.g., FT50 list). Ability to plan and manage independent research, including budgets and collaborations. Excellent spoken and written English
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to event planning processes. 2. Logistics and Operations Manage event logistics end-to-end, coordinating resources, schedules and physical set-up as per the event plan. Monitor on-the-day logistics
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly