Research Assistant Professor – Psychiatry & Behavioral Neuroscience #PBN087

The University of Chicago•Chicago, IL

About The Position

The University of Chicago’s Department of Psychiatry & Behavioral Neuroscience seeks a research appointee at the rank of Research Assistant Professor to join the Innovation in Population Health Center. The Center develops person-centered assessment tools, decision-support models, and data-driven strategies to improve behavioral health and human service systems. Its work is grounded in Transformational Collaborative Outcomes Management (TCOM) and combines research, statistical analysis, training, and implementation support to inform service planning, quality improvement, and outcome monitoring. The appointee will initially conduct academic research in collaboration with a faculty member or team on ongoing projects involving statistical and machine learning methods applied to Child and Adolescent Needs and Strengths (CANS) and other TCOM assessment data. Topics include predictive modeling, evaluation of service outcomes, and development of decision-support algorithms. Over time, the appointee will be expected to develop an independent research agenda and grant portfolio. Compensation depends on qualifications. The position is benefits-eligible.

Requirements

A doctoral degree or equivalent in epidemiology, biostatistics, or a related field before employment begins.
Completed postdoctoral training in biostatistics, epidemiology, statistical methodology, or a related quantitative research area before employment begins.
Applications must include a CV with bibliography and a research statement.

Nice To Haves

Expertise and experience in statistical methodology development.
Expertise and experience in machine learning and predictive modeling.
Experience analyzing large-scale assessment data.
Scientific programming experience in R and Python.
Experience applying these methods to behavioral health or child welfare research.
A strong publication record, collaborative research experience, a track record of research, and potential to obtain extramural funding.

Responsibilities

Conduct academic research in collaboration with a faculty member or research team.
Participate in projects developing and applying statistical and machine learning methods to CANS and other TCOM assessment data.
Contribute to predictive modeling, evaluation of service outcomes, and development of decision-support algorithms.
Over time, develop an independent research agenda and grant portfolio.