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close to the nest [1 ] but to better understand foraging, we need landscape level detail. The direction of the project can be tailored, but could include developing and applying Bayesian ML approaches
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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to continuously learn, adapt, and refine world models in self-adaptive and autonomous systems. Specifically, the research will investigate how AI-based methods can support the evolution and updating of transition
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to continuously learn, adapt, and refine world models in self-adaptive and autonomous systems. Specifically, the research will investigate how AI-based methods can support the evolution and updating of transition
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that lower the carbon and computational footprint of training and inference. Parameter-efficient fine-tuning: Harnessing large foundational vision–language models using adapters, LoRA, low-rank updates, and
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providing research updates, and assisting in the preparation of manuscripts for publication. Furthermore, you will need to assist in writing reports for the funder and, when needed, help write new project
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for 25/26 and the amount of stipend for 26/27 will be updated by UKRI in due course. View All Vacancies
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-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms
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for these tasks, enabling decision-making under uncertainty and the optimisation of long-term outcomes, such as reducing infections or fatalities. As new data are collected, RL algorithms will update