New Generation of Glucose Control Technologies Incorporating AI/ML Tools/Strategies (R01 - Clinical Trial Optional)

The summary for the New Generation of Glucose Control Technologies Incorporating AI/ML Tools/Strategies (R01 - Clinical Trial Optional) grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the National Institutes of Health, which is the U.S. government agency offering this grant.
New Generation of Glucose Control Technologies Incorporating AI/ML Tools/Strategies (R01 - Clinical Trial Optional): The purpose of this funding opportunity is to support research addressing barriers that limit progress toward more effective open- and closed-loop diabetes control systems. This may include research that addresses: 1) innovation of sensing relevant analytes/physiologic signals; 2) novel formulation and delivery of hormones; 3) artificial intelligence (AI)/machine learning (ML)-driven tools/algorithms; and 4) digital twin modeling to augment decision support and automated control systems with the goal of improving glycemic control, reducing burden of care, and enhancing quality of life of people with T1D].This NOFO is associated with the Special Diabetes Program (https://www.niddk.nih.gov/about-niddk/research-areas/diabetes/type-1-diabetes-special-statutory-funding-program/about-special-diabetes-program) which funds research on the prevention, treatment, and cure of type 1 diabetes and its complications, including unique, innovative, and collaborative research consortia and clinical trials networks
Federal Grant Title: New Generation of Glucose Control Technologies Incorporating AI/ML Tools/Strategies (R01 - Clinical Trial Optional)
Federal Agency Name: National Institutes of Health (HHS-NIH11)
Grant Categories: Health
Type of Opportunity: Discretionary
Funding Opportunity Number: RFA-DK-27-146
Type of Funding: Grant
CFDA Numbers: 93.847
CFDA Descriptions: Information not provided
Current Application Deadline: June 17th, 2026
Original Application Deadline: June 17th, 2026
Posted Date: May 7th, 2026
Creation Date: May 7th, 2026
Archive Date: August 16th, 2026
Total Program Funding: $3,000,000
Maximum Federal Grant Award: $500,000
Minimum Federal Grant Award:
Expected Number of Awards: 5
Cost Sharing or Matching: No
Last Updated: May 7th, 2026
Applicants Eligible for this Grant
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 (see text field entitled "Additional Information on Eligibility" for clarification.)
Additional Information on Eligibility
Refer to Section III. Eligibility Information in the NOFO for additional information on eligibility.Foreign Organizations/International Collaborations:Non-domestic (non-U.S.) Entities (Foreign Organizations) are eligible to apply.Non-domestic (non-U.S.) components of U.S. Organizations are eligible to apply.Foreign components, as defined in the NIH Grants Policy Statement, are allowed.
Grant Announcement Contact
Division of Diabetes, Endocrinology and Metabolic Diseases
[email protected]
[email protected]
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