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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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Can We Teach AI to Outsmart Humans in the Werewolf Game—Without Changing the AI Itself? Large Language Models (LLMs) have dazzled us with their ability to converse, code, and create—but they still
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state-of-the-art high heat flux testing, simulating the extreme environments of fusion reactors. Harness advanced computational tools to model complex particle-material interactions and predict material
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model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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application areas: the simulation of electromagnetic fields in high-speed electrical interconnects in the semiconductor industry; the prediction of the electromagnetic performance of communications devices
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Manufacturing industries face mounting pressure to reduce environmental impact whilst maintaining efficiency and competitiveness. Traditional approaches often lack real-time insights and predictive
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materials interact with the body. This project addresses that gap by engineering a 3D-printed full-thickness skin model that mimics the aging microenvironment, enabling more predictive evaluation of novel