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"Research Assistant Professor"

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Research Assistant Professor

The Institute for Social Research (ISR) at the University of Michigan invites applications for a Research Assistant Professor in the Survey Research Center (SRC), Survey Methodology Program (SMP).

As the world's largest academic social science survey and research organization, the Institute for Social Research is a leader in developing and applying new social science methods. The Institute's Survey Research Center conducts investigator-initiated, survey-based research on theoretical and applied problems of both social and scientific importance.

The Institute's SRC has been an international leader in interdisciplinary social science research involving the collection and analysis of data, especially data from scientific sample surveys, for nearly 80 years. SRC is the site of a large group of world-renowned survey methodologists that includes Fred Conrad, Michael Elliott, Sunghee Lee, Trivellore Raghunathan, Yajuan Si, Henning Silber, James Wagner, and Brady West. Together they form the Survey Methodology Program (SMP), which draws on multidisciplinary expertise from its core faculty, including statistics, survey methodology, social psychology, cognitive psychology, sociology, and computer science. SMP pursues cutting-edge statistical and methodological research with investigator-initiated research grants. SMP also houses the Michigan Program in Survey and Data Science (MPSDS), a graduate degree-granting program at the University of Michigan. MPSDS has both Master's and PhD programs.

We are particularly interested in candidates who are developing methods for acquiring and integrating non-traditional sources (e.g., contextual data, social media data, administrative data, biosocial data, sensor data) and applying AI and emerging technologies to survey methodology and broader social science research methods.

We expect that the successful candidate will:

  • Advise and support graduate students in the Michigan Program in Survey and Data Science (MPSDS)
  • Engage in regular mentoring activities and teaching opportunities in MPSDS
  • Pursue their own research interests through external funding
  • Provide regular technical guidance to the Survey Research Operations (SRO)
  • Have the opportunity to collaborate with other scientists in ongoing research programs at the University of Michigan and beyond

The Research Assistant Professor position requires:

  • A doctoral degree in quantitative social sciences or related disciplines, including but not limited to survey and data science, computational social science, statistics, data science, information science, computer science, sociology, psychology, or other social science and related fields
  • Evidence of a research agenda and demonstrated scientific interest in emerging technologies and their integration into social science research methods
  • Experience in teaching, training, or mentoring (formal or informal)
  • Strong written and oral communication skills

Although not required, the preferred applicant will also have:

  • A record of scholarship that investigates the adoption of AI and emerging technologies in social science research
  • Experience securing and managing externally funded research (e.g. pilot projects, small grants, research grants)

The position is a 12-month appointment located in Ann Arbor, MI, with an anticipated start date in Summer/Fall 2026. The salary is highly competitive.

Applications should be submitted through Interfolio at https://apply.interfolio.com/179362. Only materials submitted through the Interfolio link will be reviewed. Do not submit materials on this website or using any of the apply now links on this page.

Applicants are invited to submit a cover letter describing their interest in the position and their scholarly activities; a research statement; a statement of teaching experience; a curriculum vitae; two recent publications, and three references.

Please direct questions or inquiries to Dr. Sunghee Lee, at sungheel@umich.edu. Applications are due February 4, 2026.

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