95 programming-language "INSAIT The Institute for Computer Science" Postdoctoral positions at University of Oxford in United Kingdom
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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The Phonetics Laboratory is seeking to recruit a Postdoctoral Research Associate to carry out research on the effects of long-term language contact on prosody and other phonetic phenomena, with
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
related field. Strong quantitative and programming skills (e.g., R required). Experience in ecological modelling with different Artificial Intelligence methodologies, including but not limited to machine
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using in vivo models. The role will also include supporting the general program of research within the pre-clinical team. You will work in Containment level 2 and 3 facilities to assist with murine
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-workers. Excellent oral and written communication skills, including proven ability to write in English at a suitable standard You will experience of flow cytometry and experience with tissue culture
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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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coding experience (both Python and C/C++), and a record of working in a Linux environment and related scripting languages. What we offer At the university of Oxford your happiness and wellbeing at work is
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related discipline and have begun to establish a strong research profile, evidenced by a strong, well-cited publication record. Proficient in Python, R, BASH and/or other relevant programming languages, you
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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