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                Chris Holmes, the centre will initially focus on the following thematic areas: • Decision analysis under model misspecification • Uncertainty quantification around LLMs • Constrained 
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                and apply state-of-the-art modelling, characterisation, and machine learning techniques to understand how batteries behave and age. Collaborating with project partners, you’ll turn these insights 
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                . You must have demonstrated experience in in in vivo models of inflammatory disease and a flexible approach to dealing with research problems as they arise. You must demonstrate excellent communication 
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                to completion of) a PhD/DPhil in geotechnical engineering, along with experience in numerical methods, including the implementation of soil constitutive models in finite element code. You should have excellent 
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                Agile methodologies, particularly within areas such as CRM implementation and the rollout of a new operating model across the organisation. The Senior Project Manager will also collaborate closely with 
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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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                Chris Holmes, the centre will initially focus on the following thematic areas: • Decision analysis under model misspecification • Uncertainty quantification around LLMs 
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                on implementation studies of existing mathematical models. The post would be suitable for applicants with general interests in implementation of healthcare technologies, real-time processing in clinical 
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                effects. You will contribute to the numerical modelling part of the project, which will benefit from novel element level and centrifuge testing experimental results. You will set up and validate numerical 
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                related field with significant post-qualification research experience. You should have specialist knowledge in mechanistic interpretability, model editing, unlearning, or other methods relevant to AI safety