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-polysaccharide assemblies to naturally occurring and engineered polymers and industrially relevant catalytic materials. The Opportunity The successful candidate will lead an interdisciplinary research program in
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help shape a major research direction at the intersection of AI, neuroscience, and human health. Required Qualifications: A Ph.D. in computer science, computational neuroscience, biomedical engineering
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Qualifications: A PhD, MD/PhD, or equivalent research doctoral degree in neuroscience, biomedical data science, computer science, psychology, psychiatry, statistics, engineering, applied mathematics, or a related
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transdisciplinary network. Required Qualifications: A completed Ph.D. in a relevant field, such as the social sciences, environmental studies, sustainability science, science and technology studies, public policy
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intellectual perspectives these backgrounds bring. Prof. Lynette Cegelski is Professor of Chemistry and, by courtesy, of Chemical Engineering at Stanford and is affiliated with the Stanford Biophysics Program
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networks and will have access to the computational and informatics infrastructure required for clinical AI research. The position offers opportunities to work at the intersection of methodological
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for action ("affordances") shape neural representations, perception, and behavior. Why this position? You will sit at the center of a uniquely cross‑disciplinary team and work closely a network of
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rational strategies for next-generation therapeutics. The Lu Lab develops 2D/3D spatialtemporal omics, spatial pharmacology, and computational/AI approaches to map, model, and reprogram how cells
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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