Emerging Mathematics in Biology (eMB)
U.S. National Science Foundation · Other Federal · 25-509
Source: Grants.gov · View original posting ↗
- AwardWhat a single award can be worth — the funder's published per-award amount or floor–ceiling range.
- $6M total
- DeadlineFinal application due date.
- Not set
- Letter of intentDue date for the letter of intent (a short pre-application some funders require or request before the full proposal).
- —
- MechanismNIH activity code — the grant type (R01 research project, R21 exploratory, K series career development, F series fellowship, …).
- —
- DurationMaximum project period for a single award.
- —
- Expected awardsHow many awards the funder anticipates making under this opportunity.
- 15
- Funding cycleHow often the program accepts applications (annual, multiple cycles per year, rolling, or one-time).
- Unknown
- Open dateWhen applications open (or opened).
- Aug 17, 2026
- Total fundingThe overall pool the funder expects to commit across ALL awards under this opportunity — not what one project receives.
- $6M total
- Clinical trialWhether proposed projects must, may, or must not include a clinical trial.
- Unspecified
Research areas
Auto-classified from the title and description (keyword-based) — may be imperfect.
Description
The Emerging Mathematics in Biology (eMB) program seeks to stimulate the development of innovative mathematical theories, techniques, and approaches to investigate challenging questions of great interest to biologists and public health policymakers. It supports truly integrative research projects in mathematical biology that address challenging and significant biological questions through novel applications of traditional, but nontrivial, mathematical tools and methods or the development of new mathematical theories particularly from foundational mathematics, including the mathematical foundation of Artificial Intelligence/Deep Learning/Machine Learning (AI/DL/ML) enabling explainable AI or mechanistic insight. The program emphasizes the uses of mathematical methodologies to advance our understanding of complex, dynamic, and heterogenous biological systems at all scales (molecular, cellular, organismal, population, ecosystems, evolutionary, etc.).