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One-U RAI Cluster Hires Program

Over the last year, the One-U Responsible AI Initiative (RAI) has made significant progress in advancing its mission. This includes deploying cyberinfrastructure, growing research expertise, engaging with our community, and building the services and support structures to sustain these efforts. You can read more about these efforts in our annual report.

We are now working to define the focus areas for the cluster hires, which we hope to launch this summer. We aim to hire three or four clusters over three hiring cycles starting in the 2026 academic year. These clusters will align with RAI thematic areas, be responsive to broader opportunities, build on existing strengths and other RAI investments, and align with the priorities and plans of colleges, schools, and departments. If you would like to learn more about this effort and/or engage with us in this process, please reach out directly to the One-U RAI or someone from your college engaged with the One-U RAI.

Thematic Focus Areas

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AI-Enabled Resilience Solutions for the Environment

Accelerate the creation of solutions to and promote future resilience against environmental hazards

  • The AI-enabled Resilience Solutions for the Environment cluster will focus on the development, application and study of AI methods that accelerate the creation of solutions to and promote future resilience against environmental hazards such as wildfires, air and water quality, drought, and extreme heat, including the impacts of AI technology itself.

    • Potential Impact: Research will inform public policy around environmental hazards that will provide a sustainable future for all Utahns.
    • Example Outcomes:
      • Develop advanced risk analyses of climate impacts on health and develop social policy to mitigate potential risks.
      • Improve models of environmental risks and hazards through improvements in observational datasets, model benchmarks, and quantification of modes of variability.
      • Leverage AI for optimization of deployment of climate solutions and inform company net-zero plans, improving energy transition technologies and materials, energy system modeling, and smart and resilient power grid operation and planning.
      • Develop AI to improve near-term forecasting of climate variability and impacts, such as drought, wildfire, geological hazards, as well as early warning systems, such as rapid wildfire detection and deforestation detection, to inform decision making.
    • Complementary Expertise: Thriving culture of interdisciplinary collaborative research on environmental risks by Wilkes Center, WIRED Global Center, and the GCSC.
    • Broader Context: Growing interest in the development, applications, and societal outcomes of AI in addressing environmental risks across the state and globe.
    • Existing Datasets: Resolution Rapid Refresh (HRRR) Forecast Model, WIRED Cyberinfrastructure, Utah Wildfire Risk Explorer (UWRAP) Tool.
    • Potentially Relevant Departments: Atmospheric Sciences (Mines & Earth Sciences); City & Metropolitan Planning (Architecture + Planning); Environment, Society & Sustainability (Social & Behavioral Sciences); Communications, English, or Library (Humanities); Economics (Social & Behavioral Sciences); Eccles Institute of Economics (Business); School of Biological Sciences (Science).
    • The ARISE Cluster will include at least 2 mid-career and 4 early-career faculty hired over 3 hiring cycles. Initial hires will bring cross-cutting expertise and will provide context and direction for later hires.
    • Expertise Required:
      • AI-based modeling and forecasting of environmental risks (mid-career).
      • AI applications in environmental health assessment (mid-career).
      • AI development for environmental impact monitoring (early-career).
      • AI applications in economic impact assessment and cost-benefit analysis of adaptation strategies (early-career).
      • AI application in environmental scenario planning and policymaking (early-career).
      • Public communication and ethics regarding the use of AI in environmental management (early career).

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Health Data Operationalization for AI Research

Catalyzing AI-powered solutions across the patient care lifecycle in a safe and equitable manner.

  • This cluster will focus on advancing health data linkage and readiness to catalyze AI-powered solutions across the patient care lifecycle (bench-to-bedside and beyond) in a safe and equitable manner.

    Medical data infrastructure and AI technical solutions leveraging that data infrastructure are constantly evolving, and it is challenging for many researchers to fully leverage UU health system data, particularly multimodal EHR data. Therefore, the impact of research discoveries on holistic data-informed patient care has yet to reach its full potential.

    • Ongoing Efforts: Vivek Reddy, Reed Barney, Jim Livingston, Andrew Post, Vik Deshmukh, Ravneet Chadha, and many others are working on developing more streamlined access to the EHR.
    • Broader Context: Prior to launching a hiring effort, additional coordination across the U is required. Current efforts are focused on democratizing access to the EHR.
    • Existing Resources: Utah Population Database (UPDB), Enterprise Data Warehouse (EDW) and associated EHR data.
    • To ensure that the University of Utah can attract the best and brightest talent as part of this cluster, it would be helpful for health data to be accessible in a timely, safe, and democratized way for AI research.
      • Enable stakeholders across campus to integrate data, specifically health data, with the computing infrastructure needed for AI-enabled research.
      • Establish processes and resources to support data integration and enable the success of this cluster hiring effort.

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Rethinking Education for the AI Revolution

Understanding how AI will shape the structures and nature of teaching and learning

  • This cluster will focus on leveraging AI to shape the structures and nature of teaching and learning at the University level. It seeks to position the U to lead the nation in harnessing AI for education by:

    • Understanding how AI is transforming the future of education, work and the workplace and how responsible AI education should factor into this transformation.
    • Developing AI ethically and responsibly to catalyze the AI-transformation of education, work, workplace, and workforce.

    Potential Impact: Research will provide technical advising for the implementation of teaching for the future.

    • Complementary Expertise: The Center for Teaching Excellence, the Academic Innovation and Intelligence Lab, and other centers across campus support incorporation of future workforce development into education.
    • Broader Context: The AI revolution is transforming education as well as the nature of work, the workplace, and the workforce. In addition to the educator, we must consider both the current student and the lifelong learner. As with all revolutions, we must distinguish the immediate from the medium and long-term.
    • Existing Resources: AI focused courses and certificates in the Kahlert School of Computing and across campus.
    • Potentially Relevant Departments: Educational Psychology (Education); Computer Science (Engineering); Philosophy, Communication (Humanities); Information Technology (Business); Quinney College of Law; Psychology (Social and Behavioral Sciences).

  • The Rethinking Education Cluster will include at least 2 mid-career and 4 early-career faculty, hired over 3 hiring cycles. Initial hires will bring cross-cutting expertise and will provide context and direction for later hires. Responsible AI includes understanding ethics, security, bias, and transparency in the context of responsible AI use, development, or evaluation in the teaching environments and in work environments.

    • Expertise Required:
      • Learning with AI (mid-career and early-career): understanding how the AI revolution is affecting students, their learning, and the learning environment. For example, developing AI innovations, including virtual teaching and research assistants, to transform teaching and learning for the future.
      • Teaching with AI Innovation (early-career): research on how AI innovations in teaching and work environment influence the quality, effectiveness and value of teaching and learning based on the effect on the student learning experience.
      • Developing AI Innovations for the Classroom (early-career): implementation and development of AI tools to support the future teaching and learning.
      • Working with AI (mid-career and early-career): understanding how the AI revolution is affecting the nature of work and the workplace with corresponding implementation and development of AI tools to support the future of work.

Frequently Asked Questions

  • Goal: Establish 3-4 clusters over 3 hiring cycles starting AY’26.
    Overarching principles:
    • Align with RAI thrusts and responsive to broader opportunities.
    • Build on and complement existing strengths/aspirations and other RAI investments.
    • Align with College/School/Department priorities and plans.
    Timeline:
    • Upon approval, job postings ready by July.
    • Target home departments and hiring committees must be defined by June.
    Framework and Funding:
    • Shared hires with a faculty appointment with an academic unit and a research appointment at SCI.
    • Baseline startup package will be split evenly amongst One-U RAI, the SVPAA, and the home department; salary supported from the One-U RAI.

  • One-U RAI cluster hires will used a shared appointment model between the SCI Institute and an academic department. Shared appointments of faculty members are made in the situation where a unit that does not have the authority to appoint faculty wishes to collaborate with a faculty-appointing unit (academic department) and “share” some of the faculty member’s responsibilities. With a shared appointment, the faculty member will hold tenure/be on the tenure-track in only the academic department. The shared-appointment unit will offer input in faculty reviews, and will often provide financial or other resources for the faculty member who then conducts teaching, research, and/or service on behalf of the shared-appointment unit. More information can be found on the Office for Faculty webpage.

  • If you have ideas around specific focus areas for the cluster hires that align with the thematic areas of environment, healthcare and wellness, and teaching and learning, including examples of existing collaborative or complementary expertise existing on campus or gaps that could be filled, please reach out to the One-U RAI Team or to the One-U RAI directly at rai@utah.edu.