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data as requested by principal investigator or supervisor; use common statistical programs requiring the application of job control language in generating and organizing data. * Adapt new, nonstandard
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the supervisor outlines conceptual approach. ● Build and organize data as requested by principal investigator or supervisor; use common statistical programs requiring the application of job control language in
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principal investigator or supervisor; use common statistical programs requiring the application of job control language in generating and organizing data. • Adapt new, nonstandard methods outlined by
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the microbiota). These interactions are crucial for promoting our health but also can lead to many inflammatory diseases. We seek to understand the complex molecular language that governs these host-microbiota
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organize data as requested by principal investigator or supervisor; use common statistical programs requiring the application of job control language in generating and organizing data. Adapt new
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and in other collaborating labs at Stanford to tackle emerging clinical questions in oncology, utilizing various AI methods, predictive modeling approaches, and large language models. Specific areas
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HAI Postdoctoral Fellowship with Professor Chris Potts, Department of Linguistics Foundation Models (FMs) are enabling researchers to build AI systems at higher levels of abstraction and with lower
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understanding of current AI techniques and approaches including supervised and unsupervised machine learning, symbolic AI, and generative AI (including large language models) [technical understanding of one
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of Medicine at Stanford University. The aim of the post-doc is to study how innovations in AI, especially adaptation of Large Language Models (LLMs) architectures for time-series data, can be used in study of
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communication and collaboration with humans. This project will comprise two highly related aims. The first component of this project will entail developing grounded, joint vision and language-understanding