AIM-AHEAD & NCATS Training Program

The AIM-AHEAD & NCATS Training Program is a collaborative effort between the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) and the National Center for Advancing Translational Sciences (NCATS). The program aims to reduce barriers for researchers from diverse backgrounds to access and analyze real-world clinical data using artificial intelligence and machine learning (AI/ML) methods. As part of this effort, it offers technical training and mentorship in health data science through an 8-month hybrid program that combines self-paced courses, live classes, and hands-on experience with the National COVID Cohort Collaborative (N3C) Data Enclave. Trainees receive a stipend of $8,000 and a $2,000 allowance to attend the AIM-AHEAD 2026 Annual Meeting. The training curriculum is structured into five progressive phases: Foundations, Health Data Analysis I, II, III, and Presentations. Participants build core competencies in AI/ML, clinical data management, and ethical data use. The program includes technical instruction in AI/ML models, good algorithmic practices, causal inference, and clinical data ontologies. Mentors provide 1:1 support to guide career development and clinical advisors help interpret data meaningfully. At the conclusion of the program, participants are expected to present their work at the AIM-AHEAD Annual Meeting and contribute to the broader AIM-AHEAD and NCATS communities. Eligibility for the program is open to U.S. Citizens, Permanent Residents, and Non-Citizen U.S. Nationals residing in the United States or its territories. Applicants must hold at least a bachelor’s degree in physical sciences, life sciences, data science, public health, or related fields. Institutions must be U.S.-based. Eligible organizations include higher education institutions, nonprofits, local and tribal governments, and for-profit businesses. Individuals on temporary visas and foreign institutions are not eligible. The program strongly encourages applicants from a range of educational and professional backgrounds, including those with limited experience in either data science or clinical fields. Applications are due by September 26, 2025, at 11:59 p.m. ET. The application process requires registration as a mentee on AIM-AHEAD Connect, followed by submission through the InfoReady platform. Required materials include a biosketch, academic transcripts, a personal statement, and at least two letters of support. One letter must confirm the applicant has protected time for program participation. NIH-style formatting is required for all submissions. Optional foundational courses are available for applicants with limited exposure to programming, statistics, or health data. Program notifications will be sent by November 10, 2025, and selected trainees must accept by November 14, 2025. The program kicks off on November 17, 2025, with coursework beginning December 1, 2025. The training concludes on July 31, 2026, culminating in the AIM-AHEAD Annual Meeting. Each trainee is required to complete a data use agreement with N3C and must register with the N3C system within 30 days of award notification. Failure to complete this step will result in disqualification from the program and forfeit of the stipend. Review criteria focus on the applicant’s stated goals, background in health and data science, and readiness for team-based research. Reviewers assess the applicant's articulation of motivations, professional support, experience, and potential for long-term impact. Participation in the AIM-AHEAD and NCATS communities is expected both during and after the program. Trainees will also complete assessments and evaluations throughout and after the program to measure progress and contribute to future improvements in training delivery.

Begin N3C registration early due to multiple institutional steps. Use Chrome or Firefox for submission. Ensure letters of support confirm protected time. Avoid applying to multiple AIM-AHEAD programs if not ready to prioritize one.