90 parallel-computing-numerical-methods-"https:" Postdoctoral positions at Rutgers University
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analysis and quantitative research methods, with a focus on geospatial analysis. The Calendar Year (CY) Associate position will be awarded for a one-year period beginning in September or October 2026, with
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analysis and quantitative research methods, with a focus on geospatial analysis. The Calendar Year (CY) Associate position will be awarded for a one-year period beginning in September or October 2026, with
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EHR and administrative claims data. Leading statistical analysis (methods may include IPTW -weighted Cox regression, active comparator new-user designs, self-controlled case series, propensity scoring
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Abilities · Comprehensive understanding of statistical concepts and methods. Proficiency in at least one programing language (e.g., R, STATA ). · Excellent quantitative analytical skills, particularly in
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will lead research projects to develop, validate, improve, and apply analytical methods for the identification and quantification of metabolites relevant to chronic diseases using advanced mass
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impactful research studies focusing on medical devices, lead the drafting and implementation of research protocols, publish in scientific journals, and have opportunities to present methods and
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statistical concepts and methods are essential. Certifications/Licenses Required Knowledge, Skills, and Abilities Excellent quantitative analytical skills, particularly in longitudinal modeling
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analysis approaches and methods, including stakeholder interest and conflict analysis, benefit-cost analysis, utility rate development, program evaluation, regulatory policy analysis, and report writing o
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science, specifically in HIV prevention, substance use, and health disparities among multiply marginalized populations using multilevel methods including social network analysis, neighborhood-level
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science, specifically in HIV prevention, substance use, and health disparities among multiply marginalized populations using multilevel methods including social network analysis, neighborhood-level