AIM-AHEAD Bridge2AI for Clinical Care Training Program

The AIM-AHEAD Bridge2AI for Clinical Care Training Program is a federally funded initiative developed under the National Institutes of Health (NIH) to advance equitable applications of artificial intelligence (AI) and machine learning (ML) in the clinical care space. This program is specifically designed to equip a diverse set of professionals with the technical and analytical skills needed to work with high-resolution healthcare data derived from the Bridge2AI CHoRUS dataset. The training initiative is administered through the AIM-AHEAD consortium and delivered in collaboration with the Bridge2AI AI/ML for Clinical Care Network, engaging participants in advanced data analysis, mentorship, and applied research. Launching its second cohort for the 2025–2026 funding cycle, the Bridge2AI Clinical Care Training Program targets up to 30 graduate students, early-career faculty, healthcare professionals, and other non-academic professionals based in the United States or U.S. Territories. The program runs for eight months, from November 17, 2025, through July 2026. The official release date for applications is September 2, 2025, and all applications must be submitted by September 26, 2025, at 11:59 p.m. Eastern Time. Notification of awards is scheduled for November 10, 2025. An informational webinar was held on September 9, 2025, to guide applicants. Accepted trainees will receive a stipend totaling \$8,000, disbursed upon completion of specific milestones. In addition, trainees will receive a \$2,000 travel allowance to attend the 2026 AIM-AHEAD Annual Meeting and another \$2,000 travel allowance to participate in the Bridge2AI Annual Meeting. Throughout the program, trainees receive direct 1:1 mentorship from experienced AIM-AHEAD members and technical coaching on R, Python, Jupyter Notebook, the OHDSI tool stack, and other analytic frameworks. The program places a strong emphasis on responsible and interpretable AI, feature engineering, generative AI, and use case development, especially in clinical environments. Trainees will engage deeply with the Bridge2AI AI/ML for Clinical Care Collaborative Cloud Platform and its integrated datasets, which include trended EHR data, high-resolution waveform data, and geospatial health data. With access to these resources, participants will complete structured training modules, attend live workshops, and engage in team projects. Deliverables include a poster presentation at both annual meetings and either a submitted abstract for a health informatics conference or a manuscript for a peer-reviewed publication. The program encourages ongoing feedback from participants to improve the Bridge2AI cloud infrastructure and curricular offerings. Eligible applicants must be U.S. Citizens, Permanent Residents, or Non-Citizen Nationals affiliated with a U.S.-based institution. Individuals on temporary visas are not eligible. Organizations eligible for participation include a wide range of domestic institutions such as public and private higher education institutions, nonprofits (with or without 501(c)(3) status), local and tribal governments, faith-based organizations, and regional agencies. Applicants must hold at least a bachelor’s degree in a related field, including but not limited to data science, biological sciences, engineering, mathematics, or public health. Prior AIM-AHEAD trainees are ineligible to reapply. The application process requires completion of both registration on AIM-AHEAD Connect and submission through the InfoReady platform. Required components include a NIH biosketch or CV, academic transcripts, a two-page rationale statement, and at least two letters of support (one from a direct supervisor and one recommendation). Applicants must also complete a licensing agreement to access the CHoRUS dataset. Evaluation criteria include the applicant’s articulation of training goals, research readiness, data science experience, and community engagement intentions. The training program is built around AIM-AHEAD’s four North Stars, which include workforce development, community engagement, health outcome improvement through AI/ML, and infrastructure building. By completing the program, trainees are expected to not only advance their own research competencies but also contribute to the broader ecosystem of equitable AI/ML implementation in clinical care. Continued access to mentorship and collaboration opportunities is also available post-program to ensure sustained impact. This program serves as a foundational initiative to empower underrepresented researchers and institutions in advancing responsible, impactful AI/ML healthcare innovations.

Clearly state training goals, expected outcomes, and institutional support. Letters should confirm dedicated time and support. No reused applications from prior AIM-AHEAD programs allowed.