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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
training or fine-tuning large-scale models (e.g., LLMs, foundation models, etc.). Create a Job Match for Similar Jobs About The University of North Carolina at Chapel Hill Carolina rates among the nation’s
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foundational models to describe IDP interactions under various physiological conditions, both normal and cancer related Use these models to iteratively design, validate, and refine experiments, leading
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emotional reactions to specific sounds (“auditory triggers”). The broad project goal is to develop computational models of hearing and perception that are fitted to human perceptual measurements and that can
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: 278915095 Position: Postdoctoral Associate: Integrating political economy insights into energy modeling Description: The Peng group in the School of Public and International Affairs and the Andlinger Center
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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for safer and more effective cancer treatment. These will include the use of biochemistry, cell biology, immunology, molecular biology, proteomics, animal modeling and mouse genetics, translational medicine
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triggers?). The broad project goal is to develop computational models of hearing and perception that are fitted to human perceptual measurements and that can be used to characterize and diagnose misophonia
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models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 14 days ago
, prompt engineering, or retrieval-augmented generation (RAG) Experience deploying large-scale machine learning models in cloud environments. Create a Job Match for Similar Jobs About The University of North
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methods to ultimately let dermatologists continually update multi-modal machine learning models. Our research objectives are to 1) develop novel model editing methods for multi-modal models, with a focus on