Opportunity

Simpler Grants.gov #RFA-LM-26-004

NIH NLM Institutional Grants for Biomedical Informatics, Data Science, and AI/ML Training

Buyer

National Institutes of Health

Posted

February 18, 2026

Respond By

September 25, 2026

Identifier

RFA-LM-26-004

NAICS

611310, 541715

The National Institutes of Health (NIH), via the National Library of Medicine (NLM), is announcing a grant opportunity to support institutional research training in biomedical informatics, data science, and artificial intelligence/machine learning (AI/ML). - Government Buyer: - National Institutes of Health (NIH) - National Library of Medicine (NLM) - Purpose: - Fund predoctoral and postdoctoral training programs that advance interdisciplinary research in health and biomedical fields using computational methods - Eligibility: - Open to public and private higher education institutions, nonprofits, and federally recognized Native American tribal governments - Products/Services Requested: - Institutional research training programs in biomedical informatics, data science, and AI/ML - No specific products, part numbers, or OEMs are required, as this is a grant for training and research - Funding Details: - Total expected funding: $12,000,000 - Individual awards: $500,000 to $775,000 - No cost sharing or matching required - Unique Requirements: - Emphasis on interdisciplinary, innovative training relevant to health and biomedical research - Focus on advanced computational and AI/ML methods - No OEMs or commercial vendors are specified, as this is a grant for academic and research institutions, not a product procurement.

Description

The National Library of Medicine (NLM) invites applications for innovative predoctoral and postdoctoral training programs that prepare researchers in biomedical informatics, data science, and AI/ML. The programs may be new or renewals of existing NLM-supported programs and focus on interdisciplinary approaches to advance these fields with relevance to health and biomedical research. The goal is to equip trainees to address complex health challenges through advanced computational methods.

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