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The Mathematical Foundations of Artificial Intelligence is a grant from the U.S. Department of Energy (DOE) Office of Science and the National Science Foundation (NSF) that funds fundamental research in mathematics, statistics, and computer science to advance the theoretical underpinnings of AI and machine learning.
The program targets scientists and researchers in mathematics, engineering, and computer science across the social, behavioral, economic, and physical sciences (Assistance Listings 47. 075, 47. 049, 47.
070). Award amounts vary by project. Research areas include machine learning theory, neural network analysis, algorithmic optimization, and AI methods enabling breakthroughs in protein folding, natural language processing, drug synthesis, and other scientific domains.
The last updated date on Grants. gov was October 19, 2025.
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Opportunity Listing - Mathematical Foundations of Artificial Intelligence Mathematical Foundations of Artificial Intelligence Agency: U.S. National Science Foundation Assistance Listings: 47. 075 -- Social, Behavioral, and Economic Sciences 47. 049 -- Mathematical and Physical Sciences 47.
070 -- Computer and Information Science and Engineering Last Updated: October 19, 2025 View version history on Grants. gov 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.
*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.
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.
As of the date the proposal is submitted , any PI, co-PI, or senior/key personnel must hold either: a tenured or tenure-track position, or a primary, full-time, paid appointment in a research or teaching position at a US-based campus of an organization eligible to submit to this solicitation (see above), with exceptions granted for family or medical leave, as determined by the submitting organization.
Individuals with primary appointments at for-profit non-academic organizations or at overseas branch campuses of U.S. institutions of higher education are not eligible. Grantor contact information If you have any problems linking to this funding announcement, please contact the email address above. No documents are currently available.
Link to additional information Funding opportunity number : Cost sharing or matching requirement : Funding instrument type : Opportunity Category Explanation : Category of Funding Activity : Science technology and other research and development
Based on current listing details, eligibility includes: Not explicitly detailed, but generally targets scientists and researchers in mathematics, statistics, engineering, and computer science. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Varies Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is rolling deadlines or periodic funding windows. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
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Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
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