Mathematical Sciences: Innovations at the Interface with Computer Sciences (MSPA-MCS)
CONTACTS
PROGRAM GUIDELINES
Solicitation
07-534
As announced on May 21st, proposers must prepare and submit proposals to the National
Science Foundation (NSF) using the NSF FastLane system at
http://www.fastlane.nsf.gov/.
This approach is being taken to support efficient Grants.gov operations during this busy
workload period and in response to OMB direction guidance issued March 9, 2009. NSF will
continue to post information about available funding opportunities to Grants.gov FIND and
will continue to collaborate with institutions who have invested in system-to-system
submission functionality as their preferred proposal submission method. NSF remains
committed to the long-standing goal of streamlined grants processing and plans to
provide a web services interface for those institutions that want to use their
existing grants management systems to directly submit proposals to NSF.
Please be advised that the NSF Proposal & Award Policies & Procedures Guide (PAPPG) includes
revised guidelines to implement the mentoring provisions of the America COMPETES Act (ACA)
(Pub. L. No. 110-69, Aug. 9, 2007.) As specified in the ACA, each proposal that requests
funding to support postdoctoral researchers must include a description of the mentoring
activities that will be provided for such individuals. Proposals that do not comply
with this requirement will be returned without review (see the PAPP Guide Part I:
Grant Proposal Guide Chapter II for further information about the implementation of
this new requirement).
SYNOPSIS
This solicitation describes the opportunities available for support through the Foundation’s Mathematical Sciences Priority Area in the following category:
- Interactions between Mathematical Sciences and Computer Sciences (MSPA-MCS)
Investments in the MSPA-MCS program aim to deepen support of collaborative research in fundamental mathematics and statistics, and computer science with a focus primarily on mathematical and statistical challenges posed by large data sets, managing and modeling uncertainty, and modeling complex nonlinear systems.
|
