[AISWorld] Use Inspired AI: Tenure-Track Position at McCombs, UT Austin

[Saar-Tsechansky, Maytal]

Use-Inspired AI: Tenure-Track Position at McCombs, UT Austin


The Department of Information, Risk, and Operations Management (IROM) at the McCombs School of Business at the University of Texas at Austin invites applications for a tenure-track assistant professor position (starting Fall 2022) in the area of Use-Inspired Artificial Intelligence. We invite applications from diverse areas, including Computer Science, Information Systems/Management, Engineering, Decision Science, and Statistics.

We are particularly interested in scholars pursuing a use-inspired AI research agenda in the context of important business, organizational, and societal challenges. We seek scholars whose research agenda aims to advance AI methodology inspired by considering AI in business-relevant contexts, and where the research simultaneously informs progress in AI and advances business/organizational/societal goals.

Successful candidates will join a vibrant AI research community within McCombs and across the University, become core members of The University of Texas at Austin’s Machine Learning Laboratory (https://ml.utexas.edu/)  and contribute to The University of Texas’ at Austin’s Translational AI Cluster that serves as an interdisciplinary research platform for use-inspired AI research on campus.

A successful candidate will be expected to contribute creatively and in depth to AI  through research, teaching, and student mentorship. Ideal candidates will have a strong and active use-inspired research agenda, demonstrate strong communication skills, and will actively contribute to McCombs and to the AI community at UT Austin.

The IROM Department at the McCombs School comprises a diverse group of scholars in four areas: Decision Science, Information Systems, Operations Management, and Statistics. More information about IROM and the McCombs School can be found at https://www.mccombs.utexas.edu/departments/irom


We are committed to recruiting, employing, and supporting highly-qualified faculty members with a wide range of backgrounds, ideas, and viewpoints, and we are dedicated to the principle that individuals be respected, their voices be heard, their contributions be valued, and that they enjoy equitable access to opportunities.

We strongly encourage women, minorities, and individuals with a demonstrated commitment to diversity and inclusion to apply. Candidates may optionally include a brief discussion of how their research, teaching and mentorship will further diversity, equity, and inclusion.

Our community also values transparency and openness in science and encourages candidates to provide in their research statement a description of how these values are reflected in their work to date and their future research plans.

Application Instructions

Applicants should submit a cover letter (1 page), CV, a statement of research and teaching including accomplishments and plans (3 pages in total), an optional diversity statement (1 page), 2 representative publications, and arrange for three recommendation letters. Interfolio will require the entry of three names (and emails) for references when submitting an application – references will be contacted and asked to submit their letters.

All materials should be submitted online via Interfolio. The search committee will begin reviewing applications on November 20, 2021, and the search will remain open until the position is filled.



A background check will be conducted on applicants selected for the positions prior to appointment.

Questions should be directed to Professor Maytal Saar-Tsechansky (maytal at mail.utexas.edu), Translational AI Faculty Search Committee Chair.

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.







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Maytal Saar-Tsechansky (she/her/hers) | Mary John and Ralph Spence Centennial Professor
Information, Risk, and Operations Management | The McCombs School of Business | The University of Texas at Austin
Machine Learning Lab<https://www.ml.utexas.edu/>, Scientific Advisory Board Member