Call for Applications

AIM-AHEAD & NCATS Training Program - Cohort 3

AIM-AHEAD & NCATS Health Data Science Training Program (HDSTP) using NCATS Data and the N3C Data Enclave

Program Timeline

Program Description

The rapid advancement of artificial intelligence and machine learning (AI/ML) is transforming health and health care by dramatically accelerating the pace of scientific discovery, improving the precision of hypothesis testing, and enhancing the development of innovative, data-driven solutions to reduce the burden of disease. With the increasing availability of large, real-world health datasets, AI/ML tools now enable researchers, clinicians, and data scientists to generate actionable insights that can advance research and improve health and health care.

However, many communities and organizations across the country have yet to build the technical expertise needed to effectively leverage these powerful tools, and cannot fully realize the benefits of AI/ML. To help address this critical gap, AIM-AHEAD and the National Center for Advancing Translational Sciences (NCATS) have partnered to offer a unique, highly-focused Health Data Science Training Program. This program is designed to equip motivated early/mid-career professionals across America with the technical skills and practical experience needed to launch or expand their careers in health data science.

Through this intensive online training program, trainees will build essential competencies in AI/ML, health data analysis, and the responsible application of these tools to large, real-world health datasets. They will also expand their understanding and skills for working effectively in interprofessional, multidisciplinary teams–an essential approach for creating high-impact projects. Equipped with core health data science skills and a toolkit of hands-on experience, program graduates will be prepared to contribute to high-impact research aimed at reducing the burden of chronic disease, enhancing scientific reproducibility, and accelerating health for all Americans.

 Purpose of the Program

Upon completing the program, trainees will be able to demonstrate the following knowledge and skills for using artificial intelligence and machine learning (AI/ML) to improve health and health care:

The AIM-AHEAD consortium and the National Center for Advancing Translational Sciences (NCATS) have partnered to offer this unique training opportunity designed to reduce barriers for researchers to access and analyze real-world clinical data. This program enables participants to conduct novel research at the intersection of AI/ML and healthcare, using data collected from a broad spectrum of American communities.

Trainees will complete online courses, participate in live classes, and learn hands-on skills using the NCATS National Clinical Cohort Collaboration (N3C) Data Enclave, a secure, harmonized health data resource that has data from over 22.9 million individuals. The N3C is designed to facilitate rapid insights into diseases and supports innovative research into treatments, health care practices, and addressing gaps in healthcare. Through this program, participants will learn to ethically and effectively apply AI/ML methods to this robust data, unlocking insights that can improve health outcomes for all American communities.

Curriculum

The program's curriculum is designed to build the essential skills needed to apply AI/ML to real-world health data. Trainees progress through five phases: Foundations, Health Data Analysis (I, II, and III), and Presentations.

In the Foundations phase, trainees complete onboarding to the AIM-AHEAD Connect platform and the N3C Data Enclave, as well as complete foundational short courses in translational science, as well as optional courses tailored to learners’ needs (e.g., understanding health data, programming, or biostatistics). These courses ensure everyone has the baseline knowledge needed to engage effectively in an interprofessional/multidisciplinary health AI/ML team.

In the Health Data Analysis I, II, and III courses, trainees delve into the core approaches for using AI/ML in health care, including AI/ML models, clinical data ontologies, good algorithmic practices, and causal inference. In parallel, trainees are guided in applying these skills using real-world data from the N3C Data Enclave.

The final Presentations phase includes training in appropriately interpreting and communicating AI/ML results. All trainees are provided the opportunity to present at the AIM-AHEAD Annual Meeting. This program prepares participants to become proficient in AI/ML research, specifically geared toward advancing healthcare by tackling real-world challenges and contributing to impactful, responsible research initiatives.

Note that trainees who complete the program retain access to AIM-AHEAD Connect and N3C platforms for continued learning and research after the program concludes.

Mentorship

During the Traineeship, each trainee is paired with a career mentor from the AIM-AHEAD community, offering individualized support for professional development. The online mentoring platform AIM-AHEAD Connect (https://connect.aim-ahead.net) will be used to match mentors with awarded trainees and for mentor/trainee engagement and progress tracking. Project teams are paired with a clinical advisor to guide selection of appropriate clinical variables and meaningful interpretation of results.

Overview of AIM-AHEAD

The National Institutes of Health's AIM-AHEAD program has established mutually beneficial, coordinated, and trusted partnerships to enhance the capabilities of researchers and communities in the development of artificial intelligence and machine learning (AI/ML) models and to expand the capabilities of this emerging technology, beginning with electronic health records (EHR) and extending to other data sets to address health outcomes.

The rapid increase in the volume of data generated through EHR and other biomedical research presents exciting opportunities for developing data science approaches (e.g., AI/ML methods) for biomedical research and improving healthcare. Many obstacles hinder more widespread use of AI/ML technologies, such as the cost, finite capacity for widespread application, and limited access to adequate infrastructure, resources, and training. Additionally, lack of both extensive data and researchers in the AI/ML field heightens the risk of creating and perpetuating reliability and performance limitations with the algorithms that adversely impact AI/ML outcomes. Many institutions have the potential to contribute expertise, data, and cutting-edge science but may lack financial, infrastructural, and data science training capacity to apply AI/ML approaches to research questions of interest to them.

The National Institutes of Health is committed to leveraging the potential of AI/ML to accelerate the pace of biomedical innovation, while prioritizing and addressing the complex drivers of health outcomes. These objectives require an innovative and transdisciplinary framework that transcends scientific and organizational silos. Establishment of mutually beneficial and trusted partnerships can enhance the participation and representation of researchers and communities with limited current expertise in AI/ML modeling and application, and improve the capabilities of data curation and this emerging technology.

AIM-AHEAD North Stars

Applications to the AIM-AHEAD and NCATS Training Program must be aligned with one or more of the AIM-AHEAD North Stars:

North Star 1: Develop a representative AI/ML workforce with broad participation.

North Star 2: Increase knowledge, awareness and national-scale community engagement and empowerment in AI/ML.

North Star 3: Use AI/ML to improve behavioral health, cardiometabolic health and cancer outcomes for all.

North Star 4: Build community capacity and infrastructure in AI/ML to address community-centric health challenges.

Additional information about the North Stars may be found the AIM-AHEAD website (https://www.aim-ahead.net/)

The AIM-AHEAD Coordinating Center (A-CC) is a consortium of institutions and organizations that have a core mission to advance health across all American communities. The A-CC consists of 4 Cores:

The Leadership/Administrative Core leads the A-CC, recruits and coordinates consortium members, project management, partnerships, stakeholder engagement and outreach to develop AI/ML talented researchers in health research, and establishes trusted relationships with key stakeholders to enhance the volume and quality of data used in AI/ML research.

The Data Science Training Core assesses, develops and implements a robust data science training curriculum and workforce development resources in AI/ML.

The Data and Research Core addresses research priorities and needs by linking and preparing multiple sources and types of research data. To accomplish its mission, the Data and Research Core facilitates the extraction and transformation of data from EHR and data on lifestyle contributors to health for research use.

The Infrastructure Core assesses data, computing and software infrastructure models, tools, resources, data science policies, and AI/ML computing models to facilitate AI/ML and health research; and establishes pilot data and analysis environments to accelerate overall A-CC aims.

Program Expectations

Designed specifically for multidisciplinary and early-career professionals, training is offered in a hybrid format, through a combination of self-paced online courses and live virtual class sessions developing hands-on skills and experience using real-world health data. Trainees should commit to 8-10 hours per week, including 3-4 hours of class, team meetings, and office hours, and 4-6 hours for self-paced online learning. Each year, classes are scheduled to optimize the opportunity for all accepted trainees to participate across the various time zones (e.g., Tuesdays 4:30 - 6:00 PM Central Time).

Trainee Expectations

• Devote an average of 8-10 hours effort per week to completing program activities.
• Attend, actively participate, and complete all training sessions, courses, and learning activities.
• Engage professionally and proactively communicate with all NCATS and AIM-AHEAD instructors, staff, mentors, and advisors.
• Work with their institution to complete Institutional Data Use Agreement and individual registration for the National Clinical Cohort Collaboration (N3C) Data Enclave within 30 days of starting the program.
• Work independently to ensure on time completion of self-paced online courses.
• Work professionally, respectfully, and collaboratively within a team of peers.
• Proactively communicate questions and utilize N3C and AIM-AHEAD Help Desk support.
• Proactively document progress and on-time completion of all program goals and milestones–such as course completion, class participation, mentorship meetings, assignments, presentations at the AIM-AHEAD Annual Meeting, etc on the AIM-AHEAD Connect Program Tracker.
• Meet monthly with AIM-AHEAD Career Mentors.
• Generate an abstract suitable for submission to a conference, and/or a manuscript suitable for peer-reviewed publication.
• Present a research poster at the AIM-AHEAD Annual Meeting. The abstract and poster submitted - including the title - must comply with the US White House Executive Order.
• Respond to all program communications in a timely fashion, and proactively communicate any potential absences or changes to your status or participation.
• Engage in the NCATS and AIM-AHEAD learning communities and peer networking beyond the program, including being a positive ambassador and peer-mentor for future cohorts of trainees.
• Attend NCATS and AIM-AHEAD meetings such as webinars and seminars.
• Complete all training and programmatic forms on time.
• Provide meaningful feedback on all course and program evaluations, including periodic questionnaires after the program.
• Reside in the United States (including U.S. Territories) for the duration of the program, from November 2025 to July 2026.

National Clinical Cohort Collaboration (N3C) Data Accounts:

N3C maintains high security and privacy guidelines. All training in this program is done with synthetic or de-identified data within the N3C platform on Palantir Foundry (no data may be downloaded). Note: Trainees do not view any Protected Health Information.

All accepted trainees are required to register with the N3C within 30 days of the Notice of Award. To register, visit the N3C Registration page at the following URL and complete the steps.

• https://n3c.ncats.nih.gov/clinical-cohort/researcher-essentials/registration.

We strongly encourage all applicants to begin registration early, as there are multiple steps involved, trainees may need approvals from their institution’s signing authority, and delays may be expected during the holiday weeks. If a trainee is not able to complete registration before the Health Data Analysis courses begin in January 2026, the trainee will not be able to continue in the program and stipends will not be issued.

If you need assistance while registering, visit https://n3c.ncats.nih.gov/clinical-cohort/support to submit a help ticket or join the N3C Office Hours.

Important Notice

Consistent with NIH practice and applicable law, AIM-AHEAD programs may not use the race, ethnicity, or sex of prospective program participants or faculty as an eligibility or selection criteria. The race, ethnicity, or sex of candidates will not be considered by AIM-AHEAD in the application review process or when making funding decisions.

Eligible Organizations

Higher Education Institutions

• Public/State Controlled Institutions of Higher Education.
• Private Institutions of Higher Education.

Nonprofits

• Nonprofits with 501(c)(3) IRS Status.
• Nonprofits without 501(c)(3) IRS Status.

For-Profit Businesses/Organizations

• Small Businesses.
• For-Profit Organizations (Other than Small Businesses).

Local Governments

• State Governments.
• County Governments.
• City or Township Governments.
• Special District Governments.
• Indian/Native American Tribal Governments (Federally Recognized).
• Indian/Native American Tribal Governments (Other than Federally Recognized).

Other

• Independent School Districts.
• Public Housing Authorities/Indian Housing Authorities.
• Native American Tribal Organizations (other than Federally recognized tribal governments).
• Faith-based or Community-based Organizations.
• Regional Organizations.

The applicant’s organization must be a domestic institution/organization located in the United States and its territories.

Foreign Institutions

• Non-domestic (non-U.S.) Entities (Foreign Institutions) are not eligible to apply.
• Non-domestic (non-U.S.) components of U.S. Organizations are not eligible to apply.
• Foreign components, as defined in the NIH Grants Policy Statement, are not allowed.

Additional Applicant Eligibility Criteria

1. Candidates must be U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals.
   • U.S. Citizen: Any individual who is a citizen of the United States by law, birth, or naturalization.
   • Permanent Resident: A standing given to United States immigrants/non-citizens who can legally reside in the United States in perpetuity.
     • The applicant must have their permanent residency at the time of application (no later than September 26, 2025).
   • Non-Citizen National: A person born in an outlying possession of the United States on or after the date of formal acquisition of the United States at birth.
   • Accepted candidates must be able to submit a W-9 tax form.
   • Individuals on temporary visas (F1, J1, H1, etc.) are not eligible.
2. Current and former AIM-AHEAD Program participants (including awardees, fellows, trainees, mentors, clinical advisors, experts, and grant-writing coaches) are ineligible to apply to the current year AIM-AHEAD Training Programs, listed below:
   • AIM-AHEAD All of Us Training Program.
   • AIM-AHEAD & NCATS Training Program.
   • AIM-AHEAD Bridge2AI for Clinical Care Training Program.
   • AIM-AHEAD Bridge2AI AI-READI Training Program.
3. Applicants who apply to more than one Training Program (listed above) will be selected for only one program in which to participate based on scientific and programmatic review and applicant preference.
4. AIM-AHEAD Coordinating Center personnel (hubs/cores/offices) are eligible to participate in the Training Programs (listed above), but will not receive a stipend or travel allowance.
5. Federal employees are eligible to participate in the Training Programs (listed above), but will not receive a stipend or travel allowance.
6. Education: Applicants can be post-baccalaureate or graduate students, postdoctoral fellows, medical students or residents, allied health trainees, early-career investigators, or early-career employees as defined in the “Eligible Organizations” section above. Applicants must hold at a minimum a bachelor’s degree in one of the following or related fields:
   • Health sciences (e.g., medicine, nursing, pharmacy, psychology, health information technology, therapy, social work).
   • Public health (e.g., epidemiology, biostatistics, health administration, clinical implementation).
   • Data science, statistics, mathematics, engineering, or computer science.
   • Physical sciences (e.g. chemistry, physics).
   • Biological or life sciences (example: biology, zoology, biochemistry, microbiology).
7. Applicant Preparation and Experience: This training opportunity is open to applicants with the following experience:
   • Applicants possessing a clinical background, including those with limited exposure or experience with data science, statistics, or programming (e.g., Python or R).
   • Applicants who are proficient in data science, statistics, or programming (e.g., Python or R), including those with limited exposure or experience with clinical medicine, public health, or ethics training related to clinical data.
8. Although it is not mandatory, it is strongly recommended that applicants have some foundational understanding, and preferably hands-on skills and experiences, in the following topics to optimize learning experience and better prepare for the challenges of the program. Introductory or refresher foundational courses on these topics will be available to successful applicants at the start of the traineeship.
   • Health research, public health.
   • Health data management .
   • Translational science and real-world data.
   • Coding/programming with R or Python.
   • Probability and statistics.
   • Multidisciplinary or interprofessional teams.

Required Format:

• Arial font, no smaller than 11 points; margins at least 0.5 inches (sides, top, and bottom).
• Single-spaced lines and consecutively numbered pages.
• Submit as a single PDF document to the InfoReady application portal.

Required Elements of the Proposal

Profile Information

• Name, organization, department, position title, location during the training year (November 2025 - July 2026), area of interest, email address, and profile web page, if any.
• Confirmation of whether their institution currently has or needs a signed N3C Institutional Agreement on file (see https://n3c.ncats.nih.gov/clinical-cohort/agreements).
• Please answer all additional profile questions in InfoReady.

Letters of Support

• One signed letter of support from the applicant’s supervisor is required. The letter should confirm the provision of sufficient protected time for the applicant to fully engage with the training. The letter of support must include the supervisor’s contact information (full name, position title, organization, email/phone number, and signature), and must be on the letterhead of the supervisor’s organization.
• In addition to the supervisor’s letter, a minimum of one letter of recommendation (a second letter of recommendation is optional) is required from faculty who taught the applicant and who can attest to the applicant’s preparedness, aptitude, and rationale for advanced data analysis training. Letter(s) of recommendation should highlight relevant skills and accomplishments. Each letter must include the referee’s contact information (full name, position title, organization, email/phone number, and signature), and must be on the letterhead of the referee’s organization.
  • For applicants who hold faculty positions (assistant, associate, or full professor), a minimum of one letter is required, from the department chair or equivalent.
• Letters must include either a handwritten signature (scanned or photographed) or a digital signature. Typed signatures will not be accepted.

Academic Transcript

• Academic transcripts (official or a photocopy) must be included from applicant’s undergraduate and, if applicable, graduate programs for current students and postgraduates.

Biographical Sketch of the applicant fellow

• An NIH biosketch or CV (not to exceed 5 pages) is required. Please prepare the Fellowship Biosketch
  • NIH biosketch form: https://grants.nih.gov/grants/forms/biosketch.htm.
    • Template: Traineeship Biosketch.
    • Instructions.
    • Predoctoral Fellowship biosketch sample.
    • Postdoctoral Fellowship biosketch sample.

Statement of Rationale for Pursuing Training (900 words maximum)

• Express your interest in, passion for, and commitment to, using AI/ML to improve health and health care for all Americans. Where possible, provide an example from your work, training, or life that you feel illustrates or supports your passion and commitment.
• Describe the skills you currently have in AI/ML analyses, health care, data science, or related fields such as bioinformatics, epidemiology, pharmacy, genomics, data management, programming, or public health.
• Describe the skills you would like to learn and what you hope to accomplish through the training program, highlighting your rationale and need for acquiring these skills.
• Explain how, if accepted, you plan to apply the training and project experience to achieve your long-term research interests and career goals. Include 1 - 2 topic areas or research questions you would like to study. The research question and hypothesis submitted must comply with the US White House Executive Order.
• Lastly, describe your readiness to actively engage with the AIM-AHEAD and N3C communities, and your commitment to contributing to communal resources, empowering new users, and building collaborations within the community.

Progress and Post-Award Reporting 

Each trainee is responsible for documenting their progress and on-time completion of all program goals and milestones–such as course completion, class participation, mentorship meetings, assignments, presentations at the AIM-AHEAD Annual Meeting, etc.  Trainees must also be willing to engage in all requested assessment, evaluation, and reporting requirements before, during, and after the program, such as an initial skills assessment questionnaire and the program, course, and mentorship evaluations at baseline, mid-program, and 1-, 6- and 12-months post-program.

A Scientific Review Committee composed of AIM-AHEAD & NCATS members will apply the following criteria to evaluate proposals and select award recipients. In assigning priority scores, reviewers will apply the standard NIH 1-9 scoring range to Criteria 1, where a score of 1 indicates highest enthusiasm, and a score of 9 indicates lowest enthusiasm, based on NIH Simplified Review Framework (see: https://grants.nih.gov/grants/guide/notice-files/NOT-OD-24-010.html).

If an applicant applies to more than one training program and is accepted, they will be selected for only one program in which to participate based on scientific and programmatic review and applicant preference. Applicants will rank their preference for the programs to which they applied in the application.

Criteria 1:

1. Articulation of Expectations and Reasons for Participation: Evaluate the clarity and depth with which the applicant articulates personal expectations and motivations for joining the program. Consider how convincingly the applicant communicates the necessity and significance of the training for their career or academic ambitions.
2. Research Background and Motivation for Training: Assess the extent of the applicant’s relevant background, professional experience, or academic qualifications that support their readiness for this program. Gauge their motivation and potential to actively participate in and derive meaningful benefits from the training.
3. Long-term Application of Training: Examine the specificity and feasibility of the applicant’s plans to apply AI/ML training in their research or professional development. Look for detailed strategies that indicate a commitment to integrating the training into their long-term career or academic objectives.
4. Support from Supervisors or Mentors: Determine if the letter of support provides strong and unequivocal commitment from the supervisor, faculty, or mentor. It should confirm the provision of sufficient protected time for the applicant to fully engage with the training.
5. Reference(s) and Assurance of Success: Critique the letter(s) of reference for their effectiveness in providing a persuasive argument that the applicant is well-prepared and likely to succeed in the program. The reference(s) should highlight relevant skills, accomplishments, and the applicant's capacity for advanced training.
6. Community Engagement and Collaboration: Evaluate how well the applicant expresses a readiness to actively engage with the AIM-AHEAD community. Look for a demonstrated commitment to contributing to communal resources, empowering new users, and building collaborations within the community.

Criteria 2:

Prior Health and Data Science Experience: Assess applicant health and data science experience by reviewing education, work history, programming proficiency, project involvement, and understanding of math/statistics applied in data analysis.

To be evaluated by selecting one of the following options from a drop-down menu.

• Beginner
• Intermediate
• Expert

Criteria 2 will be applied to ensure that opportunities are accessible to individuals with varying degrees of experience, contributing to building strong interprofessional team science skills.

Applicants should expect notification of their acceptance status by November 10, 2025. Accepted applicants will receive an invitation from PaymentWorks requesting the following:

• A valid tax ID (either an EIN or SSN) via W9 for U.S. vendors.
• To upload a Bank Validation file for ACH/EFT or Wire Payments. (https://community.paymentworks.com/payees/s/article/What-Is-A-Bank-Validation-File)

Trainee Benefits

Trainees Will Receive

• An $8,000 stipend based upon successful completion of the trainee requirements.
• A $2,000 allowance to attend the 2026 AIM-AHEAD Annual Meeting.
• Access to the N3C, including the HelpDesk, training resources, and community benefits.
• Access to the AIM-AHEAD Connect platform, including the webinars, mentorship, discussion boards, and community benefits.
• Technical training and guidance from the NCATS health data science instructors.
• Programming training, office hours, and AIM-AHEAD HelpDesk support from the AIM-AHEAD Data Science Training Core.
• Professional support and guidance from an experienced career mentor.

Traineeship Stipend

Upon successful completion of the trainee requirements, participants will receive stipend support totaling $8,000* in three installments of $2,000, $3,000, $3,000, and an additional $2,000 in travel support to attend the 2026 AIM-AHEAD Annual Meeting. Date and location of Annual Meeting to be announced at a later date.

Trainee Mentorship

Each awarded trainee will receive research and career mentorship from experienced, skilled investigators selected from AIM-AHEAD members. The online mentoring platform AIM-AHEAD Connect (https://connect.aim-ahead.net) will be used to match mentors with awarded trainees and for mentor/trainee engagement and progress tracking.

Available Support from AIM-AHEAD Functional Cores

AIM-AHEAD NCATS Training Program awardees will receive support from the following A-CC cores:

Data and Research Core

The Data and Research Core will work with the awardees:

• To help prioritize data curation and linking opportunities that will be of the highest value to AIM-AHEAD Consortium members.
• To provide guidance and lessons learned on data use agreements necessary to participate as data contributors to AIM-AHEAD-sponsored programs.
• To provide feedback on proposed data governance structures and processes.

Infrastructure Core

The Infrastructure Core will provide the awardees with:

• AI/ML Assessment Tool and Maturity Model to pre-evaluate applicants or awardees and tailor the program based on their current capabilities and resources.
• Open-source data and AI/ML tools:
  • Team of solution architects, data scientists, and software engineers to provide training and mentorship.
  • Administrative and operational support models.

Data Science Training Core

The Data Science Training Core will provide support to the awardees by:

• Identifying training needs.
• Recommending customized training modules.
• Supporting HelpDesk related to data science training.
• Synergizing with ongoing DSTC programs such as practicum, professional development, and outreaches.

There will be an informational webinar on Friday, August 29, 2025, from TBD

Registration Link:

Frequently Asked Questions

Please refer to the Frequently Asked Questions document before creating a help-desk ticket.

AIM-AHEAD & NCATS Frequently Asked Questions

HelpDesk

If your question is not answered in the above FAQ document, please create a help-desk ticket using the link below.

AIM-AHEAD NCATS & HDSTP HelpDesk

AIM-AHEAD and NCATS Health Data Science Training Program Co-Directors

• Toufeeq Syed, PhD, MS, The University of Texas Health Science Center, Houston, TX
• Robert Mallet, PhD, The University of North Texas Health Science Center, Fort Worth, TX
• Aubri Hoffman, PhD, MS, National Center for Advancing Translational Sciences [c] and Axle Research & Technologies, Bethesda, MD
• Mike Kurilla, MD, PhD, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD