|Leland M. Jamesonemail@example.com||(703) 292-4883||E 8445|
|Yuliya Gorbfirstname.lastname@example.org||(703) 292-2113||E 8425|
|Malgorzata Peszynskaemail@example.com||(703) 292-2811||E 8432|
Apply to PD 16-1271 as follows:
For full proposals submitted via FastLane: standard NSF Proposal & Award Policies & Procedures Guide proposal preparation guidelines apply.
For full proposals submitted via Grants.gov: the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov Guidelines applies. (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: http://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide)
Important Information for Proposers
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 20-1), is effective for proposals submitted, or due, on or after June 1, 2020. Please be advised that, depending on the specified due date, the guidelines contained in NSF 20-1 may apply to proposals submitted in response to this funding opportunity.
Full Proposal Window
November 16, 2020 - December 1, 2020
November 16 - December 1, Annually Thereafter
Research proposals to the Computational Mathematics program submitted before the window beginning date and after the window closing date will be returned without review. Conference and workshop proposals must be submitted in accordance with the information below.
Supports mathematical research in areas of science where computation plays a central and essential role, emphasizing analysis, development and implementation of numerical methods and algorithms, and symbolic methods. The prominence of computation with analysis and ultimate implementation efficiency of the computational methods in the research is a hallmark of the program. Proposals ranging from single-investigator projects that develop and analyze innovative computational methods to interdisciplinary team projects that not only create and analyze new mathematical and computational techniques but also use/implement them to model, study, and solve important application problems are strongly encouraged.