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The Sloan Foundation's exploratory grantmaking in Technology supports the development and adoption of AI tools that accelerate scientific research and automate aspects of the scientific process.
Funded scope includes foundation models for science (chemistry, biology, materials, physics), AI lab automation and self-driving laboratories, AI-driven literature review and synthesis tools, machine learning for experimental design, scientific reasoning and hypothesis generation in LLMs, and infrastructure for open scientific AI.
The exploratory program identifies emerging sectors, tools, and methods with high potential that may not yet attract mainstream funding, prioritizing interdisciplinary risk-taking work.
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Or search similar grants →According to the current listing, eligibility includes: U.S. and international non-profit research institutions, universities, and individual investigators. Open to all career stages. Rolling letter of inquiry process; invited full proposals follow. Strong fit for projects building AI infrastructure or methodology with broad applicability across scientific domains. Confirm the full requirements in the official notice before applying.
The current listing shows grants typically range from $100,000 to $400,000 per project. Rolling letters of inquiry accepted year-round. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for Sloan Foundation Exploratory Grantmaking in Technology for AI in Science and Research Automation 2026 are due December 31, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
Sloan Foundation Exploratory Grantmaking in Technology for AI in Science and Research Automation 2026 is funded by Alfred P. Sloan Foundation. 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.
The Alfred P. Sloan Foundation's Exploratory Grantmaking in Technology program supports the development and adoption of AI tools that accelerate scientific research and automate aspects of the scientific process. Funded areas include foundation models for science (chemistry, biology, materials, physics), AI lab automation and self-driving laboratories, AI-driven literature review and synthesis, machine learning for experimental design, scientific reasoning and hypothesis generation in LLMs, and infrastructure for open scientific AI. Rolling letters of inquiry (no more than two pages) are accepted year-round and emailed to technology@sloan.org; grants typically range from $100,000 to $400,000.
Human-AI Interaction Research Grants for Multidisciplinary Research and Prototyping is sponsored by Alfred P. Sloan Foundation. This grant funds multidisciplinary research on human-AI interaction—how people interact with, are affected by, and can benefit from artificial intelligence systems—as well as prototyping of novel uses of AI systems that move research insights into practice.
Path to a Cure Research Grant is a grant from Cystic Fibrosis Foundation that funds basic science research aimed at restoring CFTR protein function or developing gene-based approaches to treat cystic fibrosis. The program specifically targets projects proposing restoration of CFTR protein function or approaches to fix or replace the defective CFTR gene. Awards provide up to $150,000 per year (plus 12% indirect costs) for up to two years, with grants competitively renewable for additional funding. Eligible applicants are independent investigators at for-profit and nonprofit institutions. Proposals must be hypothesis-driven with sufficient preliminary data. The fall cycle deadline is November 15, 2026.
Utilizing the Full Power of Empire AI is a Simons Foundation request for applications that funds basic science research making genuine use of multinode GPU clusters on New York State's Empire AI supercomputing consortium. The framing is deliberately specific: the Foundation is not funding AI applications generally but research that requires clusters of GPUs across multiple nodes, which excludes the large volume of scientific machine learning that runs comfortably on a single node and is therefore not compute-constrained. Target fields named in the call are computational astrophysics and physics, computational biology, mathematics, neuroscience and computational physical chemistry, together with foundational machine learning models, so the programme spans both AI-for-science and the development of foundation models themselves. The combination of a 6,000,000 dollar commitment across 6 to 12 awards, no published per-award ceiling, and an instruction that budgets should reflect the proposed research signals that the Foundation expects ambitious, compute-heavy proposals rather than incremental ones. The award period runs 1 June 2027 to 31 May 2029 against a 1 December 2026 deadline at 12 p.m. Eastern time. The binding constraint is institutional: eligibility is restricted to faculty at the nine Empire AI consortium member institutions, which makes this one of the most geographically concentrated significant AI funding opportunities currently open, and effectively a New York State research-capacity instrument rather than a national competition.