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and numerical methods for many-body dynamics beyond the reach of standard classical simulation. About the role We have a high preference for a candidates who is willing to start imminently, and before
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multiple ship classes and shore power applications. The project seeks to deliver industry-relevant modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world
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machine learning (ML) weather and climate models to improve physics-based models, while using physical understanding to enhance ML approaches. By integrating ideas from numerical weather prediction, climate
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of the electron gun and waveguide required to produce the high-intensity electron beams required for delivering photons in FLASH mode. • Develop and apply advanced numerical simulation and/or optimisation tools
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and waveguide required to produce the high-intensity electron beams required for delivering photons in FLASH mode. • Develop and apply advanced numerical simulation and/or optimisation tools
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focused on the use of enabling technology, such as flow, to control complex chemical processes. With projects in supramolecular synthesis, organic materials, industry processes, automated optimization, and
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optimization, and crystallisation, we are a multi-disciplinary group with expertise in chemical engineering, organic synthesis, porous materials, and automated and digital approaches. Alongside Prof Slater, you
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optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long
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of accidentals and low-energy signals, characterize background populations, improve event reconstruction, and understand detector effects. They will contribute to LZ operations and performance optimization
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of accidentals and low-energy signals, characterize background populations, improve event reconstruction, and understand detector effects. They will contribute to LZ operations and performance optimization