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, transdiagnostic cohort Integrating theory-driven models with data-driven analytic approaches to improve prediction of symptom trajectories Contributing to study-wide discussions of analytic strategy, model
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: Comparative genomics RNA secondary structure prediction Covariance models and RNA homology search methods Machine learning or artificial intelligence applied to biological data Transcriptomics analysis methods
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metadata, and clinical imaging workflows. Experience with image classification, segmentation, temporal modeling, representation learning, multimodal learning, or clinical prediction modeling. Familiarity
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genomics, multi-omics, and artificial intelligence to predict and prevent disease trajectories through precision medicines tailored to the right patient at the right time. Our vision is to create digital
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, including the PBWT/GBWT family of haplotype algorithms for biobank-scale identity-by-descent (IBD) detection, the Med-BERT and CovRNN clinical foundation models for electronic health records, and the UDIP