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learning, decision-making, belief updating) Coordinating large database of computational task data for harmonized analyses Modeling individual differences and within-person change over time in a longitudinal
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diabetes, neurocognition, and cardiometabolic disease Functional interpretation of disease-associated genetic variants Integration of single-cell multiome data (snATAC + scRNA-seq) with large-scale human
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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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at the intersection of emotion science, psychometric assessment, and large-scale intervention research. The projects will investigate the development and validation of novel measures of emotional skills and emotion
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of medication safety and effectiveness using national VA electronic health record data, including the extraction and curation of very large analytic datasets. Develop, preregister, and implement analytic plans
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evolutionarily conserved structured RNAs and candidate mammalian riboswitches from large genomics and transcriptomics datasets. The position will involve close collaboration with experimental scientists who will
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paradigm, and an NIMH study focuses on depression and anxiety genetics in large biobank samples. Through these and several other current projects, we have access to large datasets, including the Million
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allograft rejection/failure is highly desirable. The approaches are founded on interpretation of large data sets from human genome level sequence data and/or mouse cell type specific transcriptome sequence
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twins of patients, modeling disease biology across scales—from genome to cell to circuit—to enable proactive, personalized intervention. By combining large-scale human data (genomes, single-cell and
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) Analyze and interpret genetic and genomic data Participate in the clinical trial activities, including: Recruitment, enrollment, and retention of eligible study participants in hospital settings Design