Opportunity Information: Apply for RFA OD 22 027
The Advanced Training in Artificial Intelligence for Precision Nutrition Science Research (AIPrN) Institutional Research Training Programs (T32) opportunity (RFA OD 22 027) is an NIH discretionary grant aimed at launching new institutional training programs that prepare predoctoral trainees, postdoctoral trainees, or a combination of both to work at the intersection of artificial intelligence and precision nutrition. Led by the NIH Office of Nutrition Research (ONR) in partnership with participating NIH Institutes and Centers, the planned FOA focuses on building structured, cohort-based research training environments that help institutions develop a pipeline of researchers who can handle the scale and complexity of modern nutrition-related biomedical data.
At its core, the program is designed around integration. Training programs supported under this initiative are expected to blend precision nutrition science with AI methods, including machine learning, alongside systems biology, systems science, big data approaches, and computational analytics. The underlying problem NIH is targeting is that nutrition and diet-related conditions are influenced by many interacting factors, spanning biology, behavior, environment, and social context. As data sources expand across these layers, the FOA emphasizes that many of the resulting scientific questions are too complex for intuition alone, making advanced computational and AI-driven approaches essential. The long-term intent is that these training programs will enable researchers to generate insights and tools that can ultimately support innovative strategies to prevent or address diet-related chronic diseases in ways that align with the missions of the participating NIH Institutes and Centers.
The vision statement for AIPrN places heavy emphasis on workforce development and diversity. The opportunity is framed as a deliberate effort to cultivate a diverse research workforce with advanced competencies in AI, machine learning, and data science analytics, specifically applied to nutrition. The training scope described is broad, explicitly covering big data from molecular-scale measurements (for example, omics and biomarker-rich datasets), to organismal and clinical phenotypes, to community and population-level data, and even societal-scale drivers that shape diet and health outcomes. In practical terms, the opportunity signals NIH interest in training models that do more than teach coding or statistics in isolation; it is pushing for interdisciplinary researchers who can meaningfully connect computational methods with nutrition science questions and translate findings across scales.
In terms of eligibility, the opportunity is open to a wide range of U.S.-based applicant types typically allowed under NIH training mechanisms, including public and private institutions of higher education, nonprofit organizations (with or without 501(c)(3) status), for-profit organizations (other than small businesses), small businesses, and multiple levels of government (state, county, city or township, special district governments), as well as independent school districts and public housing authorities/Indian housing authorities. It also highlights eligibility for a variety of mission-relevant and capacity-building institution types and community-linked entities, including Historically Black Colleges and Universities (HBCUs), Hispanic-serving Institutions, Tribally Controlled Colleges and Universities (TCCUs), Alaska Native and Native Hawaiian Serving Institutions, and Asian American Native American Pacific Islander Serving Institutions (AANAPISIs), along with faith-based or community-based organizations, regional organizations, U.S. territories or possessions, and certain tribal governments and tribal organizations (including those other than federally recognized). At the same time, it clearly states that non-domestic (non-U.S.) entities (foreign institutions) are not eligible to apply, and non-domestic components of U.S. organizations are not eligible to apply, which narrows participation to domestic applicants and domestic project components.
Administratively, the listing identifies NIH as the agency, with the opportunity categorized under Education, Food and Nutrition, Health, Income Security and Social Services, and associated with multiple CFDA numbers (93.121, 93.213, 93.233, 93.307, 93.313, 93.361, 93.398, 93.837, 93.838, 93.839, 93.840, 93.847, 93.853, 93.865, 93.866), reflecting the involvement of multiple NIH Institutes and Centers. The record shows a creation date of 2022-10-03 and an original closing date of 2023-01-15. Award ceiling and expected awards are not specified in the provided source data, which is common for certain NIH training announcements where budgets and the number of awards can depend on appropriations, program priorities, and application pressure.
Overall, this grant opportunity is best understood as an NIH investment in institutional training infrastructure that can produce the next generation of scientists fluent in both nutrition science and advanced AI methods, capable of leveraging large, multi-scale datasets to produce actionable insights and interventions for diet-related chronic disease challenges.Apply for RFA OD 22 027
- The National Institutes of Health in the education, food and nutrition, health, income security and social services sector is offering a public funding opportunity titled "Advanced Training in Artificial Intelligence for Precision Nutrition Science Research (AIPrN) Institutional Research Training Programs (T32)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.121, 93.213, 93.233, 93.307, 93.313, 93.361, 93.398, 93.837, 93.838, 93.839, 93.840, 93.847, 93.853, 93.865, 93.866.
- This funding opportunity was created on 2022-10-03.
- Applicants must submit their applications by 2023-01-15. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501 (c) (3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For-profit organizations other than small businesses, Small businesses, Others.
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