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Postdoctoral Associate — Bioinformatics and Data Science - Stites Laboratory The Stites laboratory utilizes a variety of computational, mathematical, and experimental methods to study problems in
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implementation of SEL (social and emotional learning) and foster those skills in all stakeholders in these communities, including in families and out-of-school time. We test and refine each element of our work
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complex algorithms and predictive models and determine analytical approaches and modeling techniques to evaluate potential future outcomes. Establish analytical rigor and statistical methods to analyze
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methods. * Strong communication, analytical, and problem-solving skills. * Ability to work effectively with local, national, and international collaborators. Compensation: Commensurate with Yale
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computational methods development (e.g., multi-omics, adaptive immune receptor repertoire sequencing (AIRR-seq), scRNA-seq+BCR/TCR) and applications to understanding disease and vaccination responses in SCD
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, with at least one published first-author manuscript in pharmacoepidemiology and/or health decision science in a peer-reviewed journal. 2. Strong background in epidemiological and statistical methods
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single-cell or spatially resolved genomics methods Testing therapeutic interventions in models of ovarian and cardiovascular aging Designing in vivo lineage-tracing and labeling strategies paired with high
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topics include, but are not limited to (i) developing statistical and machine learning methods for study designs and decision-making in early detection of pancreatic cancer (ii) establishing strategies
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analysis methods, including quantitative structural analysis, network-based functional analysis, voxelwise cross-modality analyses, graph theoretic approaches, and machine-learning based applications
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, political science, statistics, econometrics or related field by the start of the appointment Advanced coursework in empirical methods, including econometrics and economics, with strong demonstrated academic