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pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses, drugs, antibodies, human lymph system, seconds, days
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to understand these dynamics. This project proposes a novel pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses
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learning, where agents share partial information to enhance resilience while preserving data/model privacy and communication efficiency. The project will also consider economic dispatch and energy trading
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
Large language models (LLMs) can read and write text and code, call tools, and follow instructions. They now allow us to build agents that plan and act over many steps instead of giving a single
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to perceive, reason and act flexibly. The models will be trained on simulated datasets to learn general behaviours, then fine-tuned for specific tasks. The aim is to create intelligent agents that could
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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? No Offer Description We have two exciting PhD positions at the intersection of formal software verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based
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Primary supervisor - Prof Mark Searcey One of the key problems in the development of new anticancer agents is specificity. How do you get the compound to the site of action in the body and avoid
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characterised at atomic resolution via crystal structures of PrfA–peptide complexes. Building on these findings, this PhD will develop peptide-based PrfA inhibitors as novel anti-Listeria agents. The project will
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efficiently model structured dynamics, such as non-Markovian stochastic processes, and adaptive agents that react to environmental stimuli. The successful candidate will explore this structure, to understand