359 cloud-computing-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Oxford
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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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Oxford’s cultural community and leads an exciting programme of public exhibitions and events to engage communities with our collections. You will work alongside colleagues in a busy, professional and
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a Level 4 Corporate Responsibility & Sustainability Practitioner Apprenticeship with GLP over 30 months. Working closely with the Environmental Sustainability Programme Manager, you will support
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Computer Science, Engineering or Data Science with professional experience demonstrating your expertise. For this role you will be working with Python and AWS so experience in these is essential. It will also be
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• Employee Assistance Programme • Discounted bus travel and Season Ticket travel loans Secondment opportunity This post could be considered as a secondment opportunity. Please discuss any secondment
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-quality, safe, accessible, and welcoming front-of-house operations across a year-round programme of arts, academic, and public events. This is a hands-on role requiring strong leadership, customer service
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normal place of work. You will join the SMOC (Stem cell signatures of Mutational Order in Colorectal Cancer) study, a Cancer Research UK–funded programme led from the University of Oxford. The project aims
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cytometry innovations. You will be responsible for carrying out an advanced flow cytometry focused research program, including but not limited to complex immune profiling, sorting and cloning immune cells
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AI Research Coordinator – University of Oxford Oxford hosts one of the world’s most extensive and diverse AI research communities, spanning fundamental AI, ethics and governance, robotics, medicine, climate science and more. That breadth is a strength, but it also requires careful...
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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