Damon Runyon Quantitative Biology Fellowship
Provides students and early career scholars with mentoring support for Damon Runyon Quantitative Biology Fellowship in biotechnology, artificial intelligence, and medical technology.
The Damon Runyon Quantitative Biology Fellowship is a three-year postdoctoral research award from the Damon Runyon Cancer Research Foundation that supports computational and quantitative scientists who aim to apply their analytical expertise to cancer research questions. The fellowship explicitly bridges two scientific communities by requiring each applicant to secure two co-mentors — one from a computational or quantitative field and one from cancer biology — ensuring that fellows develop fluency in both domains. Research must be conducted full-time at a US university, hospital, or research institution. Foreign nationals are eligible provided all work takes place in the United States.
Stipends escalate annually: $76,000 in Year 1, $78,000 in Year 2, and $80,000 in Year 3, totaling $234,000 in stipends plus $2,000 per year in expense allowances for a maximum award value of $240,000 over three years. The program prohibits the use of any award funds for indirect costs or institutional overhead — an unusually explicit restriction that makes the full amount available for direct research support. Physician-scientist applicants are also eligible for up to $100,000 in medical school loan repayment, and $1,000 per year per dependent child is available.
Eligible applicants must hold a PhD, MD, MD/PhD, DDS, DVM, or DO with the degree received no more than five years before the application deadline. Applicants cannot have worked in the primary mentor's laboratory for more than 24 months before applying. The 2026 deadline is December 1, 2026 at 4:00 PM Eastern Time. Applications are submitted through the Proposal Central platform. Competitive applications demonstrate a quantitative research background, a clearly articulated cancer biology question, and a dual-mentorship team with the institutional infrastructure to support cross-disciplinary training.
Quantitative and computational biology approaches applied to cancer research, requiring co-mentorship from both a computational scientist and a cancer biologist.
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