Submission Deadline 10/09/2026
Fiscal Capacity $8,500,000
NSF Grant — Key Facts
Opportunity Number 24-569
Agency NSF
Application Deadline 10/09/2026
Award Amount $8,500,000
Status Posted
Sector Technology
Award Floor $500,000
Cost Sharing Not Required

Fiscal Parameters & Taxonomy

Authority NSF
Status Posted

Who Can Apply

Others (see text field entitled "Additional Information on Eligibility" for clarification)

Eligibility Intelligence

*Who May Submit Proposals: Proposals may only be submitted by the following: -Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. - <span>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.</span> *Who May Serve as PI: <div class=&quot;OutlineElement Ltr SCXW177155816 BCX0&quot;> <p class=&quot;Paragraph SCXW177155816 BCX0&quot;><span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>As of the date the proposal is </span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>submitted</span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>, any PI, co-PI, or senior/key personnel must hold either:</span></span></span><span class=&quot;EOP TrackedChange SCXW177155816 BCX0&quot; data-ccp-props=&quot;{&quot;></span> <ul> <li><span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;>a tenured or tenure-track position, </span></span><span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;>or</span></span><span class=&quot;EOP TrackedChange SCXW177155816 BCX0&quot; data-ccp-props=&quot;{&quot;></span></li> <li><span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;>a primary, full-time, paid appointment in a research or teaching position</span></span></li> </ul> <span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>at a US-based campus of an organization eligible to </span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>submit</span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;> to this solicitation (see above), with exceptions granted for family or medical leave, as </span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>determined</span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;> by the </span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;>submitting</span><span class=&quot;NormalTextRun SCXW177155816 BCX0&quot;> organization. Individuals with </span></span></span><span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;>primary</span></span><span class=&quot;TrackChangeTextInsertion TrackedChange SCXW177155816 BCX0&quot;><span class=&quot;TextRun SCXW177155816 BCX0&quot; lang=&quot;EN-US&quot; xml:lang=&quot;EN-US&quot; data-contrast=&quot;none&quot;> appointments at for-profit non-academic organizations or at overseas branch campuses of U.S. institutions of higher education are not eligible.</span></span><span class=&quot;EOP TrackedChange SCXW177155816 BCX0&quot; data-ccp-props=&quot;{&quot;></span></div>

Program Description

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 Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE) will jointly sponsor research collaborations consisting of mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists focused on the mathematical and theoretical foundations of AI. Research activities should focus on the most challenging mathematical and theoretical questions aimed at understanding the capabilities, limitations, and emerging properties of AI methods as well as the development of novel, and mathematically grounded, design and analysis principles for the current and next generation of AI approaches. Specific research goals include: establishing a fundamental mathematical understanding of the factors determining the capabilities and limitations of current and emerging generation s of AI systems, including, but not limited to, foundation models, generative models, deep learning, statistical learning, federated learning, and other evolving paradigms; the development of mathematically grounded design and analysis principles for the current and next generations of AI systems; rigorous approaches for characterizing and validating machine learning algorithms and their predictions; research enabling provably reliable, translational, general-purpose AI systems and algorithms; e ncouragement of new collaborations in this interdisciplinary research community and between institution s. The overall goal is to establish innovative and principled design and analysis approaches for AI technology using creative yet theoretically grounded mathematical and statistical frameworks, yielding explainable and interpretable models that can enable sustainable, socially responsible, and trustworthy AI.

CFDA Programs

47.041 Engineering
47.049 Mathematical and Physical Sciences
47.070 Computer and Information Science and Engineering
47.075 Social, Behavioral, and Economic Sciences

Agency Contact

NSF grants.gov support grantsgovsupport@nsf.gov

✉ grantsgovsupport@nsf.gov

📞 703-292-4203

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