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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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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
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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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, and enabling data-driven improvements in patient care. You will have opportunities to apply foundation models—including large language models (LLMs) to real-world clinical data. You will work with well
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well as proficiency in data analysis and coding. Your writing and communication skills allow you to interact effectively with different academic communities. Applications should include responses to each
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well as excellent verbal and written communication skills. What We Offer Your happiness and wellbeing at work matters to us, so we offer a range of family friendly and financial benefits including
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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concept of agents has come to the fore again, prompted by the rise of Large Language Models (LLMs) – put crudely, the idea is to use LLMs, in the sense of being powerful general purpose intelligent systems
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drive such an attraction (J Chem Phys 2020, Langmuir 2022, Nature Nanotech 2024, Nature Communications 2025), now opening up many new and exciting avenues of exploration across scales, from molecules