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neutrino physics—using a combination of CMB and large-scale structure data. The analysis will rely on modified Boltzmann codes (e.g. CAMB or CLASS) and Monte Carlo techniques for parameter inference
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Award at the University of Sheffield. We aim to develop retrieval-augmented, multi-modal, and explainable Large Language Models (LLMs) for healthcare fact-checking. Reliable healthcare information is
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Physics-Informed Data Assimilation in Wall-Bounded Turbulence School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Yi Li, Dr Ashley Willis Application Deadline
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Award at the University of Sheffield. This project examines the amount of information required to effectively control a large network — such as an energy grid or a transportation system — when decisions
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engineering. Recent advances in large language models (LLMs), such as ChatGPT, GitHub Copilot, and similar systems, have shown that these models can generate computer code from short pieces of text (i.e
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training state-of-the-art large language models, such as GPT-4 [OpenAI 2023], to generate outputs that are preferable, helpful, and harmless to humans. However, state-of-the-art RLHF methods typically
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on a lattice. TDA is a technique that extracts key, persistent features from large datasets by analyzing their "shape." Instead of examining all data points in a point-cloud simulation (such as the one
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small lab-scale or prototype parts to large, complex, industry-relevant geometries is limited by dimensional accuracy, excessive forming time, material thinning/fracture, and difficulties in clamping
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that extracts key, persistent features from large datasets by analyzing their "shape." Instead of examining all data points in a point-cloud simulation (such as the one obtained from a simulation of a
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, breakthroughs increasingly depend on the ability to process vast amounts of information quickly and efficiently. But as our appetite for computing power grows, so does its cost both in terms of money and