Social Work Associate/Full Professor in AI and Data Science Cluster

University of Wisconsin Milwaukee•Milwaukee, WI

11d•$90,000 - $150,000

About The Position

Artificial Intelligence and Data Science Cluster Hire Initiative, University of Wisconsin-Milwaukee (UWM) The Social Work Department at the University of Wisconsin-Milwaukee (UWM) invites applicants to an associate or full professor, tenure-line faculty position as part of an Artificial Intelligence and Data Science Cluster Hire Initiative. UWM Academic Affairs, with generous support from the Northwestern Mutual Data Science Institute (NMDSI), is making a strategic investment in this position, providing competitive salary supplements and start-up resources. The new faculty will have expertise both in a critical social work area of research (e.g., health disparities, poverty policy, child welfare, community practice, criminal justice, etc.) and in AI and Data Science. The incoming faculty will collaborate with NMDSI to drive innovative research, strengthen community engagement, and develop AI and Data Science talent to support Milwaukee’s growth as a technology and innovation hub. Candidates with relevant expertise will also have the opportunity to affiliate as Fellows with UWM’s Connected Systems , which houses the Microsoft AI Co-Innovation Lab. Faculty in this cluster hire will be housed in UWM’s College of Community Engagement & Professions : Helen Bader School of Social Welfare and the School of Information Studies. The faculty hired for this cluster in the Department of Social Work will: Produce scholarship in an important area of social work research. Advance interdisciplinary AI and Data Science research at UWM. Collaborate with NMDSI to address real-world challenges. Innovate and expand AI and Data Science educational program

Requirements

Ph.D/Doctorate in Social Work or a related field.
Ability to meet rank of a tenured associate or full professor at UWM due to demonstrated distinguished record of research, scholarship, teaching, and service accomplishments.
Proven success in securing external research funding.
Demonstrated commitment to fostering inclusive environments and supporting the success of students, including historically underrepresented students (e.g. first-generation students, Pell-eligible students).

Nice To Haves

Proven track record of interdisciplinary research collaboration related to AI and Data Science.
Demonstrated experience in developing and sustaining partnerships with local organizations.
Strong record of community-engaged research that addresses systemic challenges, employs empowered and emancipatory approaches, and generates equitable and inclusive outcomes.
Demonstrated contributions to advancing underrepresented areas in AI, with tangible impacts on societal benefits, ethical AI development, and reducing biases and inequities in technology access.
Expertise in analyzing and addressing systemic challenges affecting urban and underrepresented populations using AI.