10 days left D U.S. National Science Foundation

Emerging Mathematics in Biology

Funding

$6.0M

Expected Awards

Deadline

Days

Hrs

Min

Sec

Mar 02, 2026

Posted Nov 8, 2024 (468 days ago)

Closes Mar 2, 2026 (in 10 days)

Grant Details

Agency

U.S. National Science Foundation

Opportunity Number

25-509

CFDA / ALN

47.049, 47.074

Opportunity Category

Discretionary (D)

Funding Category

Science and Technology (ST)

Funding Instrument

Grant (G)

Cost Sharing

No Cost Sharing (No)

Eligibility

Others (25)

*Who May Submit Proposals: Proposals may only be submitted by the following: -Institutions of Higher Education (IHEs): Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of sub-awards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.

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 supportstruly 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.).

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