POSTDOCTORAL FELLOWS FOR PSYCHOSOCIAL INTERVENTIONS AT SCALE

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We are seeking up to two Postdoctoral Fellows to participate in the complex, multidisciplinary UBelong Project funded by IES and NSF involving a series of large-scale experiments with college students across multiple universities around the relationship between belonging, growth-mindsets, and academic performance in large lecture courses and in engineering majors with a history of poor and inequitable outcomes. Postdoctoral fellows will join the UBelong Research Collaborative and would work most closely with Kevin Binning and Christian Schunn at the LRDC, a leading center for the study of learning and education.
The positions could begin as early as July 2022 or as late as October 2022 and would have the goal of building expertise in theory-grounded advanced quantitative data analysis and strong publications. Initial appointments are for two years and have the possibility of extension for an additional year, if funding is available. Applicants must have interest in doing equity-centered research and already have some experience in using multiple regression techniques in educational or psychological research. Additional desirable features in applicants: diversity in background (broadly defined), prior experience in doing research on survey data and/or large undergraduate courses, experience with advanced quantitative methods (e.g., HLM, growth curving modeling, IRT), interest and/or experience in mixed method designs, and experience in research with a focus on diversity, belonging, equity, and justice. 
Review of applications will begin immediately and will continue until the positions are filled. 
To apply to these positions, please send a cover letter that addresses the required and desirable features (as appropriate), CV, and one or more representative publications to: Kelly Tatone ([email protected]). The University of Pittsburgh is an Affirmative Action/Equal Opportunity Employer and values equity of opportunity, human dignity, and diversity. EEO/AA/M/F/Vets/Disabled.

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