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responsible for: Conducting research on sustainable and resource-efficient AI systems for edge datacentres. Creating hardware-aware search spaces for CPUs, GPUs, and accelerators, and developing multi-objective
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to the development of a multi-component framework for reliable and computationally efficient fatigue diagnosis and prognosis of steel structures. Building on the group's established expertise in virtual sensing and
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architectures for quantum superlattice devices, including embedded contacts and electron-transparent regions. Develop transfer and stacking processes for graphene, hBN, transition metal dichalcogenides and charge
Searches related to discrete element gpu
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