904 computer-science-programming-languages-"St"-"ST"-"FEMTO-ST" positions at University of Oxford
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for this post. The successful candidate will be required to develop a personal research programme in theoretical cosmology (which may include numerical modelling and/or data analysis), interacting with faculty
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Oxford EARTH (Equitable Access to sustainable Resources for a Thriving Habitat), a new strategic research programme. While this role will be based within SoGE, the other three posts span across Chemistry
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collaboration with the Head of Copyright and Licensing, a comprehensive copyright literacy programme at the Bodleian Libraries and throughout the University of Oxford. You will be required to: Respond
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at least two of the following programming languages: C++, C#, Python, Shell, Java. • Detailed understanding of the use of animal models to elucidate the circuit basis of behaviour
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collaborative culture, and ensure the digital services underpinning Oxford’s research are future-ready. About You To succeed in this role, you will bring: Substantial experience in digital portfolio or programme
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Researcher to join the Translation Biology Research Group led by Mr Alex Gordon-Weeks and Professor Kerry Fisher. The group is focussed on understanding the human tumour microenvironment (TME) and its role in
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South, the successful candidate will draw on existing datasets and behavioural science frameworks to create a large language model (LLM)-powered tool that supports key behaviour change techniques central
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Applications are invited for a Postdoctoral Researcher in Health Modelling, to work with a team working with Dr Ben Amies-Cull on a research programme on the Cities for Better Health: Child Obesity
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the Centre Manager, the Centre Director, Academic Programme Directors and Programme Managers, the postholder will be responsible for managing several large UKRI grants totalling around £60 million, plus the
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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