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Emerging Mathematics in Biology (eMB)

Status: Archived

Archived funding opportunity

This document has been archived. See NSF 25-509 for the latest version.

Important information about NSF’s implementation of the revised 2 CFR

NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website. These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.

Important information for proposers

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.

Supports research in mathematical biology that addresses significant biological questions by applying nontrivial mathematics or developing new theories — particularly from foundational mathematics, including artificial intelligence or machine learning.

Synopsis

The Emerging Mathematics in Biology (eMB) program seeks to stimulate fundamental interdisciplinary and potentially transformative research pertaining to the development of innovative mathematical/statistical/computational theories, tools, and modeling approaches to investigate challenging questions of great interest to biologists and public health policymakers. It supports 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 theories particularly from foundational mathematics and/or computational/statistical tools, including Artificial Intelligence/Deep Learning/Machine Learning (AI/DL/ML). 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, etc.).

 

 

Program contacts

Name Email Phone Organization
Zhilan J. Feng, (DMS)
Program Director
zfeng@nsf.gov (703) 292-7523 MPS/DMS
Mamta Rawat (IOS)
Program Director
mrawat@nsf.gov (703) 292-7265 BIO/IOS
David J. Klinke (MCB)
Program Director
dklinke@nsf.gov (703) 292-2890 BIO/MCB
Jeremy Wojdak (DEB)
jwojdak@nsf.gov (703) 292-8781 BIO/DEB
Amina Eladdadi (DMS)
Program Director
aeladdad@nsf.gov (703) 292-8128
Lisa G. Davis (DMS)
Program Director
lgdavis@nsf.gov (703) 292-5190 MPS/DMS

Awards made through this program

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