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. Current and emerging areas of work include: Life-course and translational aging research, including integration of aging science across the Stanford Clinical and Translational Science Award (CTSA) program
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to intervention). Researchers face these same challenges creating a bottle-neck to research at scale. While education technology companies have built products that lower the demands on teachers, many
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endoplasmic reticulum (ER) membrane. The project will combine cutting-edge approaches in functional genomics, mechanistic cell biology, cryo-electron microscopy, and protein engineering. We are particularly
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collected through ongoing SCEC programs. This data will support the postdoc's work developing and deploying multimodal models to improve the iFIND tool, extending its current text-based approach. Primary
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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