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AI-ENGAGE launched by NSF in early 2026 as a Quad-nation collaboration with Australia, India, and Japan supporting multinational research teams developing AI tools for agriculture, engineering, scientific discovery, and societal benefit. The inaugural cohort of six awards totaled $6M+ across the four partner countries.
Funded research includes precision agriculture AI, AI for crop disease detection in tropical climates, multinational AI safety benchmarks, and AI for scientific discovery. Strong fit for U.S. universities with established collaborations in Quad partner countries. The program expects continued cycles in 2026-2027 with growing scope.
Strong fit for AI faculty working in food systems AI, climate-resilient crop AI, AI for scientific discovery, and engineering AI with international research partners.
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Or search similar grants →According to the current listing, eligibility includes: U.S. universities and nonprofit research institutions that partner with eligible counterparts in Australia, India, and/or Japan. Multi-PI international consortia required. Each partner country funds its own researchers; NSF funds U.S. components. Industry partners welcomed as collaborators. Confirm the full requirements in the official notice before applying.
The current listing shows first cohort: more than USD 6,000,000 across partner countries supporting 6 initial multinational awards. Individual U.S.-led teams receive approximately USD 500,000 to USD 1,500,000 per project over 3-4 years. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for NSF AI-ENGAGE Quad Nations Partnership with Australia India and Japan for International AI Research Collaboration in Engineering Agriculture and Science are due December 31, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
NSF AI-ENGAGE Quad Nations Partnership with Australia India and Japan for International AI Research Collaboration in Engineering Agriculture and Science is funded by U.S. National Science Foundation in partnership with the Australian Research Council, India Department of Science and Technology, and Japan Science and Technology Agency. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
NSF NRT Advancing Ethical AI Through Convergent Research supports interdisciplinary graduate training programs that prepare PhD and master's students to research and develop ethical, trustworthy AI systems. Funded programs span fairness, privacy, safety, inclusivity, AI auditing, AI policy, sociotechnical AI research, and human-centered AI. Each training program engages multiple departments (computer science, engineering, social sciences, humanities, law, public policy) and produces a new generation of AI researchers prepared to address equity and well-being. Funded sites have included UT Austin's NRT-AI and Stony Brook's Bias-NRT. Awards provide approximately $3M over 5 years to support graduate trainees, curriculum development, professional development, and convergent research projects. Strong fit for university consortia building interdisciplinary AI ethics graduate programs.
NOAA-OAR-CIAO-2026-32786 establishes a new Cooperative Institute for the Northern Gulf of America (CINGA) advancing NOAA mission research with explicit focus on AI/ML for environmental modeling, weather and ocean forecasting, and hazard prediction. Priority research themes include improving forecasting capabilities for weather, ocean, and hazards using AI-enhanced numerical and foundation models, AI-ready data infrastructure and data stewardship, and AI/ML applications for climate-driven environmental change in the Gulf of America region. Individual task orders typically range $250K to $5M across 5+ year cooperative agreement. Strong fit for university consortia with marine and atmospheric science programs and applied AI/ML capabilities.
NSF's Expanding K-12 Resources for AI Education Dear Colleague Letter invites current NSF awardees to request supplemental funding up to $300,000 (or 20% of their original budget) to scale and expand established AI education activities into new K-12 settings. Eligible work includes producing open-access AI curricula, teacher professional development, AI literacy modules, student-facing AI tools, evaluation studies of AI education programs, and partnerships with school districts. The DCL aligns with the Presidential AI Challenge and broader federal priorities to accelerate AI literacy in U.S. schools. Awards are issued as supplements to existing grants administered by the Directorate for STEM Education and adjacent NSF directorates.
On July 22, 2026 the White House committed more than $5 billion to expand the Genesis Mission — the government-wide 'AI for science' effort — announcing 278 first projects and 14 National Science and Technology Challenges spanning health, energy, infrastructure, manufacturing, and national security. More than 15 federal agencies are contributing awards, datasets, and facilities. Here is how the pieces fit, why it reorganizes the science-funding map, and how to position for the awards flowing out of it.
Read articleOn July 22, 2026 the White House announced more than $5 billion and 15-plus federal agencies behind the Genesis Mission — a national effort to point AI at the hardest problems in science. Here is what the mission actually funds, how its five National Science and Technology Challenges map to real solicitations already opening, and how a research team or small business should position for the money flowing out of it.
Read articleNSF just committed $380 million to build a national network of AI-programmable, remotely operated laboratories — the Programmable Cloud Laboratories Test Bed (NSF 25-541), the agency's flagship contribution to the Genesis Mission. Twenty nodes, four years, self-driving experiments in chemistry, biology and materials. Here is what it funds, who is eligible, why the 'existing facilities only' rule matters, and how researchers and companies should position for what comes next.
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