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Chief - Division of Cancer Data and Artificial Intelligence

Job in Vineyard, Utah County, Utah, USA
Listing for: Association-American Cancer Institute
Full Time position
Listed on 2026-02-16
Job specializations:
  • IT/Tech
    AI Engineer, Data Science Manager, Data Scientist, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Location: Vineyard

  • Chief - Division of Cancer Data and Artificial Intelligence
Cancer Center Jobs Chief - Division of Cancer Data and Artificial Intelligence Huntsman Cancer Institute at the University of Utah

February 9, 2026

The University of Utah’s Department of Oncological Sciences (Onc Sci), in partnership with Huntsman Cancer Institute (HCI), invites applications for a visionary and mission-driven senior faculty member to serve as Chief of the newly established Division of Cancer Data and Artificial Intelligence (CDAI).

Located in Salt Lake City, Utah, Huntsman Cancer Institute is home to more than 300 cancer-focused research teams and nationally recognized programs spanning laboratory research, clinical cancer research, cancer control, population sciences, and cross-disciplinary disease centers. HCI is the only NCI-designated Comprehensive Cancer Center in the Mountain West, rated in the “exceptional” range at its two most recent NCI reviews. HCI serves Utah, Idaho, Montana, Nevada, and Wyoming through integrated research, clinical care, and community outreach.

With more than one million square feet dedicated to research and patient care, HCI uniquely integrates cancer hospitals with research buildings to accelerate translation from discovery to practice.

This tenure-track appointment will be hired immediately and is anticipated to be based in the future at HCI’s new state-of-the-art comprehensive cancer campus in Vineyard, Utah, scheduled to open in 2028.

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HCI Vineyard Campus:
Supported by the State of Utah and extraordinary philanthropic investment, the HCI Vineyard Campus represents HCI’s first major expansion beyond Salt Lake City. It will improve access to cancer care for central and southern Utah and foster new collaborations with academic and entrepreneurial partners, including Utah Valley University (UVU), Brigham Young University (BYU), and regional technology and AI companies within the Silicon Slopes ecosystem.

The Vineyard Campus will serve as a hub for innovation in cancer care and research, advancing four interrelated focus areas:

  • Health Care Transformation
  • AI, Data Science & Digital Oncology
  • Innovation in Clinical Trials
  • Community Outreach & Engagement
  • Together, these areas form an integrated ecosystem designed to pioneer new models of cancer prevention, diagnosis, and treatment across the Mountain West.

    Role Overview

    The CDAI Division directly supports the Vineyard Campus focus on AI, data science, and digital oncology and anchors the Department’s strategy to harness machine learning and artificial intelligence to transform cancer research and care. The Division Chief will lead the integration of computational oncology with basic, translational, and clinical research, working closely with leaders across Vineyard and the Salt Lake City campus.

    Key Responsibilities
    • Define and execute the strategic vision for the CDAI Division
    • Recruit and mentor faculty and build transdisciplinary teams across campuses
    • Lead an externally funded, innovative research program in AI-driven cancer biology, diagnostics, and/or therapeutics
    • Foster collaborations with academic partners and technology leaders
    • Translate data science innovations into real-world clinical applications
    • Represent the Division nationally and internationally
    Qualifications
    • Associate or Full Professor with a PhD, MD, or equivalent
    • Demonstrated leadership in AI or data science applied to healthcare
    • Strong publication and extramural funding record

    Preferred expertise includes machine learning, generative AI, large language models, and integration of molecular, clinical, imaging, and population-level oncology data.

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