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U.S. National Science Foundation

Directorate for Mathematical and Physical Sciences
     Division of Mathematical Sciences

Directorate for Social, Behavioral and Economic Sciences
     Division of Behavioral and Cognitive Sciences
     Division of Social and Economic Sciences

Directorate for Biological Sciences
     Division of Environmental Biology

National Institutes of Health

    National Institute on Drug Abuse

Centers for Disease Control and Prevention

    Coronavirus and Other Respiratory Viruses Division

Full Proposal Deadline(s) (due by 5 p.m. submitting organization’s local time):

     July 14, 2025

Important Information And Revision Notes

The only changes are to the due dates and addition of CDC/CORVD as a partner agency. The members of the Working Group have been changed (with different contacts from SBE/BCS and NIH/NIDA, and addition of the POC from CDC).

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is

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Synopsis

NSF is committed to securing the nation’s research enterprise as part of its core mission. The Research on Research Security (RoRS) program will advance the understanding of the full scope, potential, challenges, and nature of the research on research security field through scholarly evidence.

Background

The following activities provide background and context for developing proposals to submit to the RoRS program.

Program Description

Collectively, the research that RoRS funds will foster a broad community that builds collaborations between the STEM research community, research security researchers, and research security practitioners. Interdisciplinary approaches are encouraged, and proposers should address how they will leverage the range of expertise, theories, and methods of the team to engage in evidence-based research on research security. Proposers are encouraged to identify collaborators across a wide range of sectors, and to consider projects in collaboration with international partners that share U.S.

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U.S. National Science Foundation

Directorate for Technology, Innovation and Partnerships
     Translational Impacts

Full Proposal Deadline(s) (due by 5 p.m. submitting organization’s local time):

     Proposals Accepted Anytime

Table Of Contents

Summary of Program Requirements

Introduction Program Description Award Information Eligibility Information Proposal Preparation and Submission Instructions Proposal Preparation Instructions Budgetary Information Due Dates Research.gov/Grants.gov Requirements NSF Proposal Processing and Review Procedures Merit Review Principles and Criteria Review and Selection Process Award Administration Information Notification of the Award Award Conditions Reporting Requirements Agency Contacts Other Information Important Information And Revision Notes

Proposers must have either 1) received a prior award from NSF in a scientific or engineering field relevant to the proposed innovation that is currently active or that has been active within five years from the date of the NSF National I-Corps Teams proposal submission or 2) have participated

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Synopsis

Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology.  

The National

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