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NIH Bridge2AI Stage 2 represents the next phase of the NIH Common Fund Bridge to Artificial Intelligence program building on $130 million invested in Stage 1 to create ethically sourced AI-ready biomedical datasets. Stage 2 shifts focus from data generation to building tools devices and safety frameworks that translate those datasets into clinical and research applications.
Two interconnected initiatives are funded: Innovation Funnels supporting teams that use Stage 1 AI-ready datasets to create practical tools including diagnostic algorithms drug discovery platforms and clinical decision support systems that demonstrate measurable health impact and a Network for AI Health Science developing safety measures validation protocols and responsible-use frameworks for AI in health research.
The program values interdisciplinary teams combining computational scientists with domain experts in specific disease areas. Stage 2 Requests for Applications are expected by mid-2026. This is distinct from ARPA-H programs which fund specific high-risk clinical AI applications and from AHRQ healthcare AI safety grants which examine existing AI impact on healthcare systems.
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Or search similar grants →According to the current listing, eligibility includes: Researchers with expertise in both AI/ML methods and specific disease areas at US institutions of higher education research institutes and eligible nonprofits. Bridge2AI explicitly values interdisciplinary teams. Stage 2 RFAs have not yet been posted but are expected by mid-2026. Monitor commonfund.nih.gov/bridge2ai/funding for announcements. Confirm the full requirements in the official notice before applying.
The current listing shows $130 million over four years for Stage 2 pending availability of funds. Stage 1 invested $130 million to create ethically sourced AI-ready datasets. Stage 2 will fund Innovation Funnels translating those datasets into clinical tools and a Network for AI Health Science developing safety and validation protocols. Individual award amounts will be specified in forthcoming RFAs. Verify award ceilings, matching requirements, and allowable costs in the official notice.
NIH Bridge2AI Stage 2 Innovation Funnels and Network for AI Health Science is funded by National Institutes of Health Common Fund. 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.
Applications go through the funder's official portal — the Apply Now link on this page goes there directly.
Stanford-SLAC CryoEM Center (S2C2) User Access is sponsored by National Institutes of Health Common Fund. The Stanford-SLAC CryoEM Center (S2C2) provides access to state-of-the-art cryoEM instruments for data collection towards atomic resolution structure determination of biochemically purified single particles. It aims to enable scientists across the nation to become independent cryoEM investigators. Project applications are reviewed monthly.
National Center for CryoEM Access and Training (NCCAT) User Access is sponsored by National Institutes of Health Common Fund. NCCAT provides researchers access to state-of-the-art equipment, technical support, and instruction for the production and analysis of high-resolution data using cryo-EM technology. It also works to develop an expert workforce of cryo-EM practitioners.
Pacific Northwest Cryo-EM Center (PNCC) User Access is sponsored by National Institutes of Health Common Fund. The Pacific Northwest Center for Cryo-EM (PNCC) is a national user facility offering free access to state-of-the-art workflows for single particle analysis and electron tomography for new and experienced cryo-EM researchers. It also provides individual and group training related to sample analysis and optimization, grid preparation and screening, (semi-)automated data collection, image analysis, and 3D reconstruction.
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.
Air Force SBIR topic DAF26BZ03-DV020 seeks advanced AI-driven solutions for a scalable fleet management platform coordinating humanoid, mobile, and industrial robots performing aircraft maintenance and sustainment. Requirements include autonomous AI-based task allocation, real-time monitoring, human-robot collaboration workflows, dynamic scheduling, multi-modal sensor fusion for situational awareness, and operational optimization. Solutions must scale across mixed robotic fleets in active Air Force maintenance environments and contested logistics scenarios.
Air Force SBIR topic DAF26BZ03-DV019 seeks AI-driven solutions for fall detection, impact mitigation, and autonomous recovery technology for humanoid robots in military maintenance, logistics, and hazardous operations environments. Goals include reducing damage from falls, improving robot reliability under unstructured operational conditions, enabling safe human-robot collaboration in mixed teams, and developing predictive ML models that anticipate failure modes before they occur. Applicable to aircraft maintenance, ground sustainment, and contested logistics use cases.
Avoid common NIH grant proposal mistakes including vague specific aims, weak methodology, and poor budget justification that lead to rejection.
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