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captured either by globally over refining the mesh, which is computationally expensive and environmentally inefficient, or by running multiple successive simulations to iteratively adjust the mesh. Both
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emerged to make meshing more flexible by allowing elements to span across multiple CAD faces without explicitly modifying the geometry. However, these ideas have not yet been developed in high-order
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the mesh, which is computationally expensive and environmentally inefficient, or by running multiple successive simulations to iteratively adjust the mesh. Both approaches raise computational cost, energy
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, and travel related to the project. Overview ReNU+ is a unique and ambitious programme that will train the next-generation of doctoral carbon champions who are renowned for research excellence and
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, and travel related to the project. Overview ReNU+ is a unique and ambitious programme that will train the next-generation of doctoral carbon champions who are renowned for research excellence and
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description High-order solvers offer clear accuracy
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programme that will train the next-generation of doctoral carbon champions who are renowned for research excellence and interdisciplinary systemic thinking for Net Zero. The ReNU+ vision is that they will
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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of £20,780 (2025/26 UKRI rate). An additional allowance will be provided to contribute towards consumables, equipment, and travel related to the project. Overview ReNU+ is a unique and ambitious programme
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the most energy‐intensive infrastructures in modern economies, with their demand projected to rise sharply as digitalisation, artificial intelligence (AI), and cloud computing expand. This growth presents