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to evaluate reliability, transportability, uncertainty, information sufficiency, and clinical usefulness. The postdoc will work with large-scale clinical data resources at Stanford and through national data
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, and Heart Failure Discovery We are seeking a postdoctoral fellow to work at the intersection of cardiovascular medicine, biomedical data science, artificial intelligence, genetics, imaging, and multi
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-cultural identity development and adolescent adjustment across diverse cultural and national contexts. The position offers an exciting opportunity to work as part of a large, collaborative, interdisciplinary
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primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science, and
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identifying structures and processes that promote high quality healthcare. Our projects apply advanced analytical methods to large databases of primarily structured electronic health record data and EHR usage
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demonstrated ability to design, train, and deploy large-scale models Expertise in at least one of computer vision, speech recognition, or multimodal learning, with experience in real-world technology deployment
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gain training in 2D/3D spatial multi-omics, single-cell spatial pharmacology, AI-enabled tissue analysis, and translational cancer biology, with access to large, high-quality, in-house spatial datasets
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and practitioners. The fellow may also be expected to contribute to flagship projects on how AI can improve access to trustworthy political information, including nonpartisan large language model (LLM
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researchers approach their data, share their scientific results with other investigators, and execute scientific experiments. Stanford is home to leaders in open science, and we are embarking on a large
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National Institutes of Health T32 grants based at Stanford and the VA Big Data-Scientist Training Enhancement Program (BD-STEP). For more senior postdoctoral candidates with MD degrees who may be