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associated with biological membranes, including large peripheral membrane complexes and integral membrane transporters. In parallel with mechanistic structural work, we develop targeted chemical probes
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, biomedical data science, genetics, and AI. (5) Lead and contribute to peer-reviewed manuscripts, conference presentations, and grant proposals. Required Qualifications: PhD, MD, MD/PhD, or equivalent doctoral
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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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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
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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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QUALIFICATIONS: PhD in computer science, electrical/biomedical engineering, statistics, applied mathematics, or a related field. Strong track record in machine learning/deep learning with imaging data