118 phd-mathematical-modelling-population-modelling Postdoctoral positions at Rutgers University
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modeling approach for identifying community & policy-level interventions to address the impact of structural racism and discrimination on adolescent substance use and mental health. In this role
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the generation and characterization of novel transgenic mouse models and application of in vivo and in vitro experimental systems. Among the key duties of this position are the following: Engages in scientific
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research project entitled, A community driven modeling approach for identifying community & policy-level interventions to address the impact of structural racism and discrimination on adolescent substance
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experiences and individual differences, as well as cognitive modeling of decision-making in both lab and realworld settings. Successful candidates will be supported in building a research program at the
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combining these tools with theoretical models, we aim to understand how the brain supports thoughts, emotions, and decisions—and how disruptions or biases in these processes impact mental health. CAHBIR
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: Designs, performs and analyzes experiments using specific laboratory techniques that involve flow cytometry analysis, animal models of airway and brain disorders, single-cell RNA-seq, multiplex cytokine
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Policy, Code of Conduct, and Conflict of Interest Policy. Understands, demonstrates, and models the Rutgers Cancer Institute of New Jersey core values. Keeps abreast of all pertinent federal, state and
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/benefits/benefits-overview . Posting Summary The Modeling Equitable and Accessible Spaces for Everyone (M-EASE) project in the Department of Psychology and Center for Cognitive Science is seeking a post
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statistical issues: Which variables should be included in each model? From what population should the comparison group be drawn? Which statistical procedures should be used to determine the comparison group
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population should the comparison group be drawn? • Which procedures should be used to determine the comparison group? • Which models should be use for the data analysis? • How should missing values in the data