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and deep learning methods for large-scale genomic, clinical, and imaging biobank data, with stable multi-year NIH support. The Zhi Laboratory has a sustained track record of methods development
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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projects focusing on hPSC differentiation, and modeling neurodegenerative diseases. ● Standardize and optimize advanced molecular and imaging assays ● Analyze complex multi-omic or functional datasets
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target trial emulation. Experience designing and conducting analyses in large-scale health datasets derived from electronic health records, administrative claims, or other routinely collected data. Strong
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. Responsibilities The postdoctoral associate will – based on their research interest – contribute across one or more of the lab’s five core themes: Genomic Technology: Design and execute new, large-scale experimental
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impactful scientific projects and manuscripts. The successful candidate will contribute to multi-center projects that integrate population science, molecular epidemiology, and large-scale cohort analysis
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grammar. The postdoctoral associate will – based on their research interest – contribute across one or more of the lab's five core themes: Genomic Technology: Design and execute new, large-scale