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Large Language Models (LLMs), Agentic Systems, as well as strong interdisciplinary teamwork skills and communication skills. About the Stanford NLP Group: Stanford NLP Group focuses on basic scientific
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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 aging, health span, and longevity
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scholars to join our research team. Specializing in infectious disease epidemiology and public health modeling, we study vaccine-preventable infections (e.g., SARS-CoV-2, pertussis) and neglected tropical
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language processing (NLP) to augment clinical decision-making and expand access to high-quality healthcare. Our lab develops new methods to improve model trustworthiness and leverages heterogeneous clinical data
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happens, and how it can be made better. This is a role for someone who’s excited to work with big, messy, real-world data — and who wants to do more than just build models. We’re looking for a researcher
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are currently supporting hundreds of schools around the United States (and abroad) with a more efficient and precise approach to assessment. Through this research practice partnership model we are also able
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learners, both ambitious and team players, have a strong research publication record, and high-level understanding of programming and computer modeling. This postdoc position offers a motivated researcher an
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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focus on combination therapies that account for cell-cell interactions. Experimental model systems will include cancer cell lines, organoid/assembloid models, and clinical tumor samples. Projects will
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psychometric and longitudinal models for study of human development. The postdoctoral scholar would be advised by Prof. Nilam Ram (link is external) (Psychology & Communications, The Change Lab (link is