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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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these changes to make predictions from large scale remotely sensed data (such as satellites). This approach also allows the natural landscape variation to be used as a proxy for climate-driven tundra change
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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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incorporated into models. Your work will help fill this critical gap, improving predictions of how grasslands will respond to future change. Your project will combine large-scale data synthesis, ecological
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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