Postdoctoral Researcher

The Department of Instructional Technology and Learning Sciences invites applications for a Postdoctoral Fellow II to conduct research in early career researcher career pathways as they relate to skill development and issues of cognition and motivation. This position will require using advanced statistical methods to complete project aims. The supervising faculty member will be Dr. David Feldon ( [email protected] ), with opportunities for collaboration in the Emma Eccles Jones College of Education and Human Services. This position is guaranteed for one year with the option to renew pending satisfactory performance. This is a full-time position eligible for excellent benefits including: 13 paid holidays, earn 22 days of paid leave and 12 days of sick leave per year, 50% tuition reduction for employee and spouse, competitive health benefits packages and a generous contribution of 14.2% (without an employee contribution) into your retirement. The position is remote-work eligible. Review of applications will begin June 5, 2023. Responsibilities Participate in ongoing academic research, grant writing, and other scholarly activities including but not limited to advanced quantitative and/or psychometric analysis and scholarly publication. Lead and assist in data analysis using item response theory, structural equation modeling, latent growth modeling, or other techniques as appropriate. Provide mentorship to graduate or undergraduate research assistants. Minimum Qualifications An earned doctorate in Education, Psychology, Cognitive Science, Learning Sciences, or a related field by the date of appointment. A background in educational psychology/learning sciences, STEM education, and/or higher education. Prior experience with measurement theory, multilevel statistical modeling, and/or mixed models analysis. Fluency in English. Preferred Qualifications Track record of publications analyzing antecedents and consequences of skill development. Familiarity with and/or development of mixed methodological analysis approaches, including critical quantitative (QuantCrit) perspectives. Familiarity with multiple cohort research design and assessment. Prior experience with survey data management. rec