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Bridge2AI Stage 2 advances NIH's flagship biomedical AI initiative from creating ethically sourced, machine-learning-ready datasets to delivering deployable AI tools for specific health challenges.
Stage 2 funds Innovation Funnels that use the Stage 1 AI-ready datasets (voice biomarkers, clinical cardiology, salutogenesis, AI/ML for precision public health) to build diagnostic algorithms, drug discovery platforms, and clinical decision support systems. It also establishes a Network for AI Health Science to develop safety protocols, responsible AI implementation guidance, and ethics frameworks for biomedical AI.
Strong emphasis on FAIR data principles, transparent model documentation, equity, and public trust.
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Or search similar grants →According to the current listing, eligibility includes: U.S. universities, academic medical centers, non-profit research institutions, hospitals, and federal labs. Multidisciplinary consortia required for major mechanisms. International partners allowed under standard NIH terms. Strong fit for teams combining AI methods with deep biomedical domain expertise. Confirm the full requirements in the official notice before applying.
The current listing shows bridge2AI total program commitment is over $130 million through completion. Stage 2 awards span multi-year cooperative agreements (U54, U24, U01 mechanisms) with individual project budgets typically ranging from $1,000,000 to $10,000,000 across project periods. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for NIH Common Fund Bridge2AI Stage 2 Innovation Funnels and Network for AI Health Science are due November 30, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
NIH Common Fund Bridge2AI Stage 2 Innovation Funnels and Network for AI Health Science is funded by NIH Common Fund (National Institutes of Health). 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 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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