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Page explicitly states 'Application Cycle Closed'; deadline was September 29, 2025 at 5:00 p.m. ET, which matches stored deadline.
The Cornell Atkinson Center for Sustainability, in partnership with The 2030 Project and Cornell AI Initiative, offers fast grants of $10,000-$25,000 per research team for projects at the intersection of AI and climate science. Funded projects must address one of three themes: decarbonization of the AI stack, AI policy and governance for climate transparency, or AI applications for climate challenges.
All proposals must identify at least one specific external funding opportunity to pursue, making these seed grants designed to accelerate larger research-to-impact pipelines.
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Or search similar grants →According to the current listing, eligibility includes: Open to Cornell University-eligible Principal Investigators. Allowable expenses include personnel (excluding faculty salary), travel, data collection, workshops, professional writing support, and strategic planning. Faculty salary, laptops, and staff bonuses are prohibited. Confirm the full requirements in the official notice before applying.
The current listing shows $10,000 - $25,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
The published deadline was September 29, 2025, which has passed. Check the official notice for any future application windows before investing time in a proposal.
Cornell Atkinson AI and Climate Fast Grants is funded by Cornell Atkinson Center for Sustainability. Verify program details on the funder's official page before applying.
Yes — this listing is flagged as national in scope, so applicants across the U.S. may apply, subject to the sponsor's other eligibility criteria.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The CU System Sprint Grant is a small, fast-turnaround internal award supporting faculty who want to develop and implement AI-driven pedagogical strategies within a single course. At up to $20,000 with an explicit allowance for one course buyout, it is designed around the real obstacle facing most faculty experimenting with generative AI in teaching - not equipment or data, but protected time. The single-course framing is a deliberate scoping decision and applicants should respect it: proposals aimed at curriculum-wide or program-level transformation are a poor fit, while a concrete plan to redesign one course around AI tools and measure the effect on student learning outcomes matches what the program is asking for. Eligibility extends to tenured and tenure-track faculty as well as full-time and instructional series faculty across all CU campuses, which is broader than many internal research awards and deliberately includes teaching-focused appointments who are often closest to the pedagogical questions at issue. This is the second of three planned cycles, so faculty who miss the October 16, 2026 deadline should expect a third round. For a grants database this entry is narrower in eligibility than a federal or foundation program - it is open only to CU faculty - but it is a useful example of the institutional micro-grant tier that has grown rapidly as universities respond to generative AI in the classroom, and comparable programs now exist at many university systems. Faculty at other institutions who find this relevant should check whether their own provost or academic affairs office runs an equivalent scheme, since these internal AI teaching grants are frequently under-advertised and undersubscribed relative to external competitions.
The MIT Generative AI Impact Consortium (MGAIC) is an MIT-wide initiative bringing together industry partners and MIT faculty to advance generative AI research with high real-world impact. The consortium awards seed grants to MIT-led research teams across priority areas including: multimodal tactile sensing for robotics, real-time collaborative AI agents (e.g., jam_bots for live human-AI musical improvisation), understanding how LLM agents deviate from human choices and decision-making, foundation models for scientific discovery, generative AI for design and engineering, AI for healthcare and biology, and AI-augmented education. Each consortium funding cycle issues call for proposals from MIT faculty, with industry partner alignment guiding priority areas. Industry members include Analog Devices, Coca-Cola, OpenAI, Tata, Cisco, TWG Global, SK Telecom, McKinsey, Citi, and Verizon. Selected projects benefit from industry collaboration, data sharing, compute access through partner companies, and pathway to commercialization or real-world deployment. The consortium is hosted by MIT Schwarzman College of Computing in partnership with MIT Sloan and benefits from cross-MIT participation including CSAIL, Media Lab, and MIT-IBM Watson AI Lab.
PAR-27-054 and PAR-27-055 replace COBRE's Phase 1-2-3 model with two tracks: a 15-year Development-Expansion-Sustainability path and a 10-year Expansion-Sustainability path. At $1.5 million per year in direct costs across 23 states and Puerto Rico, with new research cores prohibited in the final phase and eligibility capped at three active awards per institution, the track you claim is the most consequential choice in the application.
Read articleMeyer pulled $6.5 million from a $200 million endowment. Greater Washington has deployed $14.3 million and put more than $1 million into Bridge Grants for 21 organizations — explicitly including mergers and organizational sunsets. Boston is funding merger facilitation. Here is how to tell which product an RFP is offering, and how to apply for the one you actually want.
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