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Field
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between things, developing the mathematical and computational foundations for optimal information transfer between surfaces and volumetric current distributions in complex scattering media. The research
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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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stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
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oriented, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's most pressing issues. In research and academic
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. You will be working at the intersection of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping
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industrial adoption of high-order CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low
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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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, you will develop the numerical, geometric and algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically
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and specialising in flood-risk evaluation, geohazard assessment, and sustainable drainage solutions across the UK, China, and Australia. This research develops a data-intensive, AI-driven framework