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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
Identification and Precision Oncology Center’. Work on the project at RENCI involves developing approaches for the use of Large Language Models (LLMs) to process collections of clinical notes, radiology and
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(LLMs), and Vision Language Models (VLMs) Develop Retrieval Augmented Generation (RAG) models Developing manifold learning (e.g., tSNE and UMAP), unsupervised clustering Analyze electronics health record
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will combine BERT type models and Large Language Model (LLM) to examine research publications and patents, in combination with other unstructured data sources such as policy documents, media, call
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, designing tools for data interpretation and visualization, and/or training foundation models and large language models (LLMs) for diverse biomedical applications. Dr. Li has published over 70 peer-reviewed
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(LLMs), and Vision Language Models (VLMs) Develop Retrieval Augmented Generation (RAG) models Develop manifold learning (e.g., tSNE and UMAP), unsupervised clustering Analyze electronics health record
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Your Job: ...to revolutionize applications of the physical sciences through tailored large language models (LLMs) and intelligent agentic systems. Your tasks in detail: Development and fine-tuning
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mathlib) for Automated Discovery in Physics Agentic frameworks (e.g. LLMs with tool-use) for closed-loop idea generation for physics (example here ) Other projects are certainly possible too. In general, we
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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The post holder will be expected to have a PhD in law or political science and skills to handle large dataset and conduct quantitative analysis and other empirical analysis. Familiarity with LLM (large
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, this will involve the design and characterisation of a novel configurable hardware LLM accelerator. The successful candidate will be able to lead the continued design and implementation