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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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actors. The developed algorithms will be validated using simulation testbeds and simple hardware-in-the-loop microgrid setups with battery storage. Overall, this research will advance the state of the art
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functions). Explore model-based RL approaches that integrate learned models with planning and adaptation mechanisms. Hybrid Evolutionary-RL Framework Develop novel frameworks with evolutionary algorithms
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summary Join an international team developing scalable algorithms to solve numerical linear algebra challenges on supercomputers. Modern high-performance computing increasingly relies on hardware
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including: * Algorithmic game theory * Approximation algorithms * Automata and formal languages * Combinatorics and graph algorithms * Computational complexity * Logic and games * Online and dynamic
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to maximise early outbreak detection. Active intervention: developing decision-making algorithms that recommend effective public-health interventions. Reinforcement learning (RL) provides a natural framework
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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-cases of classical supercomputers, the development of quantum CFD algorithms will be of widespread benefit upon the arrival of fault-tolerant quantum computing. This project involves the adaptation
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will include race videos, rider power and speed data, and race commentary to codify key race events, using expert knowledge and available evidence. - Develop a post-race analysis framework, process, and
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person. Aims: The project aims to develop and evaluate AI methods for medical image analysis to detect diabetic retinopathy, glaucoma, cataract and age-related macular degeneration (AMD). As