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Faculty Position: Early Cancer Detection and Interception Physician and Scientist Investigators
Remote / Online - Candidates ideally in
Phoenix, Maricopa County, Arizona, 85083, USA
Listed on 2026-10-26
Phoenix, Maricopa County, Arizona, 85083, USA
Listing for:
Mayo Clinic
Full Time, Remote/Work from Home
position Listed on 2026-10-26
Job specializations:
-
Software Development
AI Engineer (Applied/Software)
Job Description & How to Apply Below
Medical Specialties
Location:
Phoenix, AZ, United States
Schedule:
Full time Posted:
Hours / Pay Period: 80
Benefits Eligible:
Yes Remote worker: 100% On-Site Schedule Details: daily
Union Job:
No
Area of Interest 1:
Cancer Center Marketing Category 1:
Scientist International Assignment:
No
Up to two positions may be filled, with appointments based at either Mayo Clinic Arizona or Mayo Clinic Rochester.
Position Description The Mayo Clinic and Mayo Clinic Comprehensive Cancer Center (MCCCC) in Phoenix and Scottsdale, Arizona, and in Rochester, Minnesota, invite applications for open-rank faculty positions in Early Cancer Detection and Interception with strong leadership potential and a commitment to advancing cancer research into clinical impact. Mayo Clinic and MCCCC have built a world-class research hub to transform cancer patient care through actionable target discovery and next-generation discovery and therapeutic platforms.
These foundational platforms include Mayo Clinic Platform ((Use the "Apply for this Job" box below).) which holds the fully digitized longitudinal patient records from 15M Mayo Clinic patients and 54M patients from across the nation and the globe with collaborating healthcare institutions, which facilitate research through development of longitudinal patient cohorts prior to and following disease diagnosis. Mayo Clinic has also developed very strong AI-driven discovery, translational, and clinical programs with exceptional “on premises” and cloud-based computational resources.
These AI programs focus on multimodal virtual cell and organ predictive modeling, digital twins, and AI-based early cancer detection and interception, drug discovery, and guided intervention. MCCCC also created one of the nation’s first National Cancer Institute (NCI)-Designated Cancer Center Research Programs focused on early cancer detection and interception:
Cancer Risk Assessment, Early Detection, and Interception (REDI).These positions will focus on leading transformative spanning from discovery to translational to clinical science efforts in cancer early detection, prevention, and data-driven translational and clinical. This role will integrate discovery and translational science, clinical care, genomics, advanced analytics, and community engagement to redefine how cancer is detected early, monitored, and prevented. Particular focus will be in the field of cancer interception – delaying or preventing cancer development or progression of nascent disease—particularly in pre-cancer and high-risk populations.
Strategic Role and Leadership Expectations The Early-Detection and Interception Physician and Scientist Investigators will:
Design and lead prospective cohorts of healthy individuals and high-risk populations to study cancer initiation, progression, and interception.
Develop strategies to identify individuals at risk (pre-cancer) and guide them to appropriate screening, prevention, and early intervention pathways. Integrate clinical, genomic, and longitudinal health data to monitor disease evolution and enable early therapeutic intervention and clinical trials. Partner with institutional programs (e.g., Mayo Clinic Platform, the Mayo Clinic AI Program, the Center for Individualized Medicine, the Department of Laboratory Medicine and Pathology, Clinical Genetics) to implement comprehensive, clinical-grade genomic sequencing initiatives or other AI-based multimodal predictive algorithms for early cancer detection and guiding interception in community-based screening and clinical settings.
Expand cancer risk evaluation to include AI-driven, multi-parametric models incorporating multiple variables (eg. longitudinal clinical data, radiomics, digital pathology, genetics/genomics, exposome, epidemiology…
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