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NSF SaTC 2. 0 (Security Privacy and Trust in Cyberspace) is the largest open solicitation for university-led cybersecurity research in the federal portfolio now expanded with AI security as an explicit priority area. The 2.
0 reboot added generative AI security open-source software security quantum computing security and supply chain security as topics of interest addressing the bidirectional role of AI as both a cybersecurity threat and a defensive tool.
Research awards support adversarial machine learning and attacks on AI systems AI weaponization against people information and systems privacy-preserving machine learning and responsible AI use for detecting and responding to cyber threats. The program funds three award types: Research awards up to $1.
2M for four years Education awards up to $500K for three years and Seedling awards up to $300K for two years through Dear Colleague Letters. Proposals are accepted on a recurring annual basis with two windows per year. This is distinct from NSF CyberAICorps which focuses on scholarship and workforce development and from NSF AIMing which focuses on AI formal methods and mathematical reasoning.
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Search similar grants →Based on current listing details, eligibility includes: U.S. institutions of higher education including two-year and four-year colleges and community colleges and non-profit non-academic research organizations located in the U.S. PIs must hold a tenured or tenure-track position or a primary full-time paid research or teaching appointment. Per-PI rolling 12-month limit of 4 total proposals maximum 2 RES 1 EDU and 1 SEED. Proposals due last Monday of September and January each year. NSF solicitation 25-515. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Approximately $60 million per year supporting about 75 awards annually. Research (RES) awards up to $1,200,000 for up to four years. Education (EDU) awards up to $500,000 for up to three years or $600,000 with education research collaboration. Seedling (SEED) awards up to $300,000 for up to two years. Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is January 26, 2026. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
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.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Improving Undergraduate STEM Education: Education & Human Resources (IUSE: EHR) Program is sponsored by National Science Foundation (NSF). This program promotes novel, creative, and transformative approaches to generating and using new knowledge about STEM teaching and learning to improve STEM education for undergraduate students. It supports projects that bring recent advances in STEM knowledge into undergraduate education, adapt, improve, and incorporate evidence-based practices, and lay the groundwork for institutional improvement in STEM education. Professional development for instructors to ensure adoption of new and effective pedagogical techniques is a potential topic of interest.
Innovations in Graduate Education (IGE) Program is sponsored by National Science Foundation. The IGE program encourages the development and implementation of bold, new, and potentially transformative approaches to STEM graduate education training. It seeks proposals that explore ways for graduate students to develop skills, knowledge, and competencies needed for a range of STEM careers.
Fire Science Innovations through Research and Education (FIRE) program is sponsored by National Science Foundation (NSF). This program invites innovative multidisciplinary and multisector investigations focused on convergent research and education activities in wildland fire. It supports research that can inform risk management and response, adaptation, and resilience across infrastructures, communities, cultures, and natural environments. Relevant topics include developing novel materials and methods for retrofitting existing buildings and remediating buildings following wildfire and smoke events.
The FDT-BioTech program is a joint NSF NIH and FDA initiative that catalyzes biomedical technological innovation through foundational development of methods and algorithms relevant to digital twins and synthetic humans. The program supports inherently interdisciplinary research projects that underpin the mathematical and engineering foundations behind the development and use of digital twins and synthetic data in biomedical and healthcare applications with a particular focus on digital in silico models used in the evaluation of medical devices and to advance regulatory sciences. Priority research areas include computational representations of physiological systems verification validation and uncertainty quantification transferability and generalizability across populations ethics security and privacy considerations and validation mechanisms for digital twin models. The program incorporates AI and machine learning as key enabling technologies for creating responsive digital twin models. All proposals must address regulatory science benefits and ethical implications. This program is distinct from the NSF SCH Smart Health program which focuses broadly on AI for health research and from ARPA-H programs which target specific clinical applications.
NSF TechAccess AI-Ready America is a major new initiative to establish AI-ready Coordination Hubs in every U.S. state and territory to expand access to AI knowledge tools training and capacity building. Announced March 25 2026 the initiative is a joint effort of NSF USDA National Institute of Food and Agriculture (NIFA) Department of Labor and Small Business Administration (SBA). Each Hub will connect local partners and coordinate AI deployment scale proven approaches based on state and local priorities and address three key gaps: workforce AI literacy small business and local government AI adoption and hands-on learning pathways. Up to 56 Hubs will be funded at up to $1 million per year for three years selected through three rounds of competition. An informational webinar is scheduled for April 14 2026. This is distinct from NSF ExpandAI which focuses on institutional AI research capacity building and from NSF Expanding AI Career which targets skilled technical workforce opportunities.
The Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH) program is a joint NSF-NIH initiative supporting transformative high-risk high-reward research advances in computer and information science engineering mathematics statistics and behavioral or cognitive research to address critical biomedical and public health challenges. The program requires fundamental scientific or engineering contributions spanning two or more disciplines. Key research themes include fairness and trustworthiness in AI and ML systems for health applications multimodal sensing systems advanced analytics for biomedical research cyber-physical systems robotics and biomedical image interpretation. Projects must demonstrate how computer science or data science advances enable new approaches to biomedical or public health problems. Traditional disease-centric medical or clinical studies are out of scope. This program is distinct from the NSF ExpandAI program which focuses on capacity building and from ARPA-H programs which fund specific clinical applications. The SCH program specifically targets foundational interdisciplinary research at the intersection of AI and health sciences.