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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.
The NEH Humanities Research Centers on Artificial Intelligence program funds the creation of university-based humanities research centers focused on the ethical, legal, and societal implications of artificial intelligence. Funded centers undertake interdisciplinary humanities-led research that brings ethics, law, history, philosophy, anthropology, religious studies, literature, linguistics, and cultural studies to bear on questions raised by AI systems. Topics include responsible AI governance frameworks, AI and civil rights, AI and labor, cultural impact of generative AI, AI and creative authorship, philosophical foundations of machine reasoning, history of AI thought, and humanistic evaluation of AI safety and alignment. Centers are expected to convene researchers, train new humanities scholars in AI, host public-facing programming, and produce publications and translational tools that inform policy and public understanding. Strong fit for universities seeking to launch sustained interdisciplinary AI humanities research programs in partnership with computer science and other STEM departments.
SaTC 2.0 is NSF's flagship cybersecurity, privacy, and trust research program reorganized to address emerging threats including generative AI security, open-source software security, quantum computing security, and supply chain security. Funded scope covers the bidirectional role of AI as both a cybersecurity threat (adversarial attacks on AI systems, AI-enabled cyberattacks, data poisoning) and a defensive tool (AI for intrusion detection, automated vulnerability discovery, secure-by-design AI). The program supports interdisciplinary collaboration across computer science, mathematics, social and behavioral sciences, and STEM education to build trust in global cyber ecosystems.
NSF SaTC 2.0 (Security Privacy and Trust in Cyberspace) is the largest open solicitation for university-led cybersecurity research in the federal portfolio now expanded with AI security as an explicit priority area. The 2.0 reboot added generative AI security open-source software security quantum computing security and supply chain security as topics of interest addressing the bidirectional role of AI as both a cybersecurity threat and a defensive tool. Research awards support adversarial machine learning and attacks on AI systems AI weaponization against people information and systems privacy-preserving machine learning and responsible AI use for detecting and responding to cyber threats. The program funds three award types: Research awards up to $1.2M for four years Education awards up to $500K for three years and Seedling awards up to $300K for two years through Dear Colleague Letters. Proposals are accepted on a recurring annual basis with two windows per year. This is distinct from NSF CyberAICorps which focuses on scholarship and workforce development and from NSF AIMing which focuses on AI formal methods and mathematical reasoning.
The NIH Common Fund launched PRIMED-AI — Precision Medicine with AI: Integrating Imaging with Multimodal Data — as five coordinated funding opportunities (RFA-RM-27-011 through -015) that build a full pipeline from standards to clinic. Here is how the Playbook, Data-to-Model partnerships, Model-to-Clinic translation, Validation Center, and Logistics Center fit together, what each pays, who is eligible, and how to position before the October 2026 deadlines.
Read articleAvoid common NIH grant proposal mistakes including vague specific aims, weak methodology, and poor budget justification that lead to rejection.
Read articleThe NIH Director's New Innovator Award (DP2, RFA-RM-27-002) closes August 17, 2026, offering roughly 30 Early Stage Investigators up to $475,000 in direct costs a year for five years — $2.375 million total — to pursue bold ideas without the preliminary data a standard R01 demands. Here is how the High-Risk, High-Reward program actually evaluates applications, who qualifies, and why the usual R01 instincts will sink you.
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