Call for Applications

AIM-AHEAD All of Us Training Program - Cohort 3

Traineeship in Advanced Data Analysis using the All of Us Database

Award Details

The application of artificial intelligence and machine learning (AI/ML) to large datasets is dramatically expanding the capacity for hypothesis testing impacting the biomedical and healthcare industries. The AIM-AHEAD All of Us Training Program is intended to increase research in AI/ML by leveraging the All of Us data and infrastructure (Researcher Workbench). The central goal of the training program is to increase research in AI/ML by training individuals from various backgrounds who are committed to gaining proficiency in AI/ML data analysis and applying their expertise to speed up health research and improve the health of all communities.

Purpose of the All of Us Training Program

The following are the objectives for trainees upon completing the AIM-AHEAD All of Us Training Program:

The AIM-AHEAD consortium (Data Science Training Core and Communications Hub), All of Us, and RTI are partnering to offer AIM-AHEAD stakeholders, trainees, mentees, and consortium partners a training opportunity designed to increase researcher participation in AI/ML by leveraging the All of Us data and infrastructure (Researcher Workbench).

The Researcher Workbench is a cloud-based platform where registered researchers can access Registered and Controlled Tier data. Its powerful tools support data analysis and collaboration. Researchers use the Workbench to access, store, and analyze data for specific research projects. Researchers can perform high-powered queries and analysis within the All of Us datasets using R or Python via the integrated, cloud-based Jupyter Notebook environment.

Using the AIM-AHEAD Connect Platform, this 8-month training program will engage a group of 25 graduate students, postdocs, early-career faculty, healthcare professionals, and other non-academic professionals. Trainees will use the Dataset Builder to search, extract, and organize health information from the All of Us database, and use the Cohort Builder to create, review, and annotate data from All of Us human subject cohorts. Trainees will also receive training and technical assistance related to R, Python, Jupyter Notebook/R Studio, and model development for All of Us data subsets in the Researcher Workbench. Training will include:

• Merging/validating data across All of Us sources.
• Building a supervised model.
• Splitting data into subsets for model training and testing.
• Considering anomalies that may be present and detected or missed by the model.
• Validating the model.

The training, which uses All of Us data collected from a broad range of American communities, is directed particularly toward investigators conducting research at the intersection of AI/ML and health differences. Trainees can work independently, with another trainee who has similar interests, or a community partner.

Potential research topics that could be addressed include, but are not limited to:

• Examining statistical variation in non-medical factors affecting health such as geographic and socioeconomic status and intersectionality;
• Examining statistical interactions between family health history and lifestyle factors; and
• Using statistical analysis to identify economic, environmental, and heritable determinants of clinically significant diseases and syndromes.

Genomics Track: New for Cohort 3 of the All of Us training program is the specialized genomics track. This track is designed to support trainees interested in conducting genomic research using complex ML tools, thus is dedicates more time and resources to developing foundational and applied skills in genomic data science. A subset of trainees (up to five) will have the opportunity to participate in the Genomics Track. Applicants applying for the Genomics Track are required to meet additional criteria as described in the Eligibility Criteria for Applicants and Institutions section (page 7). Applicants not selected for the Genomics Track will still be considered for the General Track.

Please refer to the following resources to determine if your desired research topic is viable to pursue in the All of Us training program:

Having received advanced practical training in coding, model development, hypothesis testing, and data cleaning and analysis, trainees completing this program will be well equipped to harness AI/ML approaches to conduct hypothesis-driven analysis of complex datasets. The trainee will join the growing community of AI/ML professionals passionately committed to extend the benefits of AI/ML to all communities.

• Applications are due September 26, 2025 at 11:59 pm Eastern Time.
• Training through AIM-AHEAD Connect will begin on November 17, 2025, and conclude July 31, 2026.

Overview of AIM-AHEAD

The National Institutes of Health’s AIM-AHEAD Program was established to create mutually beneficial and coordinated partnerships to empower researchers and communities across the United States in the development of AI/ML models and enhance the capabilities of this emerging technology, beginning with electronic health record (EHR) data.
The rapid increase in the volume of data generated through EHRs and biomedical research presents exciting opportunities for developing data science approaches, such as AI/ML methods, to enhance biomedical research and improve healthcare. Several challenges hinder the widespread adoption of AI/ML technologies, including high costs, limited capability for broad application, and inadequate access to necessary infrastructure, resources, and training. Additionally, there is a lack of comprehensive and high-quality AI-ready data, as well as a shortage of a pipeline of talented researchers in both industry and academia, to harness the potential of AI/ML in advancing biomedical research and the practice of medicine. Furthermore, tackling the complex drivers of health outcomes require an innovative and transdisciplinary framework that transcends scientific and organizational silos. Mutually beneficial and trusted partnerships can be established to empower researchers and communities across the United States in AI/ML application.

AIM-AHEAD North Stars

Applications to the All of Us 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/)

AIM-AHEAD is a nationwide network of institutions and organizations designed to build AI talent among researchers and clinicians, support multidisciplinary research projects that harness AI/ML to improve the health of Americans and enhance the AI capabilities and infrastructure of communities or hospitals that otherwise would not have had the resources or the capacity to benefit from the advance of AI/ML. The AIM-AHEAD Coordinating Center (A-CC) is comprised of four key areas or "Cores" to drive this mission:

Program Expectations

To achieve the traineeship objectives, trainees are required to participate in all components of the program. The training program offers a Fundamental and Advanced tier for the General Track. The course and data use case training requirements are different for the Fundamental and Advanced training tiers. Trainees in the General Track will select the appropriate tier based on their skill level and experience upon acceptance into the training program. Trainee expectations are outlined below, including the additional expectations for trainees accepted into the Genomics Track.

Trainee Expectations

• Complete the required courses on the Researcher Workbench, R, Python, and Jupyter Notebook/R Studio for the trainee’s tier (Fundamental vs. Advanced).
• Engage with an AIM-AHEAD career/professional development mentor (to be assigned through the program).
• Engage in AIM-AHEAD learning communities and the peer networking sessions.
• Complete the data use case training on model building for analysis, data splitting for algorithm training and testing, addressing anomalies in model development, and model validation.
• Work with the Researcher Workbench and ML coaches and use the resources provided by AIM-AHEAD if additional training on R, Python, or AI/ML is needed.
• Conduct a research project using All of Us data and present a work-in-progress research poster at the AIM-AHEAD Annual Meeting. The project submitted - including the title - must comply with the US White House Executive Order.
• Complete and submit by the deadlines all five program evaluation surveys (conducted at program beginning, midpoint, and conclusion, and at 6 and 12 months post-program).
• Take an active role in the AIM-AHEAD network by engaging in upcoming events, webinars, and office hours; seeking additional mentorship, or becoming a mentor; and applying to current and future programs and Calls for Proposals.
• Reside in the United States (including U.S. Territories) for the duration of the program, from November 2025 - July 2026.

Additional Trainee Expectations for the Genomics Track

• Actively participate in all three genomics courses and associated activities (up to 6 hours). Trainees in the Genomics Track are not required to take the programming courses in R or Python because they must demonstrate proficiency in R or Python to be eligible for the Genomics Track.
• Meet regularly with their assigned genomic coach during biweekly group 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.

Education

Applicants must have received at least an undergraduate degree, but can be post-baccalaureate or graduate students, postdoctoral fellows, medical students or residents, allied health trainees, early-career investigators or early-career employees of non-academic institutions as defined above. Applicants must hold at a minimum a Bachelor’s degree from an accredited U.S. institution in one of the following or related fields:

• Physical sciences (e.g. chemistry, physics).
• Biological or life sciences (e.g. biology, zoology, biochemistry, microbiology).
• Mathematics or statistics.
• Data science.
• Engineering.
• Health sciences (e.g. pharmacy, psychology, health information technology).
• Public health (epidemiology, biostatistics, health administration, clinical implementation specialists).

Applicant Prior Knowledge, Skills and Experience

This training will be most beneficial for individuals who have accomplished one or more of the following. Although these experiences are not mandatory for applicants, evidence of one or more of these experiences will be considered by the trainee selection committee:

• Successfully completed an undergraduate or graduate course in probability and statistics.
• Has practical applied experience in coding/programming with R or Python.
• Has experience in data manipulation and management gained through coursework and/or research projects.
• Has practical experience with Bayesian analysis and maximum likelihood estimation.

Introductory or refresher courses on these topics will be available to successful applicants at the start of the training program, via the AIM-AHEAD Connect platform.

Additional Applicant Prior Knowledge, Skills and Experience for Genomics Track

In addition to the knowledge, skills, and experience described above, applicants to the Genomics Track must provide evidence of the following, which will be taken into consideration by the trainee selection committee:

• Demonstrated proficiency in R or Python.
• General understanding of genetics and genomics as it relates to the applicants’ field of study (i.e., undergraduate course work or practical experience).
• Prior experience in genomics computational applications as it relates to the applicants’ field of study.

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

• Provide your name, organization, department, position title, research area, email address, and profile web page.
• Please answer the profile questions on 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. Letters of support must include the referee’s contact information (full name, position title, organization, email/phone number, and signature).
• 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. Letters of recommendation should 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 the applicant’s undergraduate and, if applicable, graduate programs for current students and postgraduates.

Biographical Sketch of the Applicant

• The applicant’s NIH biosketch or CV (not to exceed 5 pages) is required.
• NIH biosketch form: Biosketch Format Pages, Instructions, and Samples | Grants & Funding.
  • Template: Traineeship Biosketch.
  • Instructions.
  • Predoctoral Fellowship biosketch sample.
  • Postdoctoral Fellowship biosketch sample.

Statement of Rationale for Pursuing Training: General Track

Provide a personal statement of not more than 900 words. Please provide a response for each item below. The trainee selection committee will consider each of these items when reviewing your application. Include each of the following sections in your response.

1. Describe what you hope to accomplish through the AIM-AHEAD All of Us Training Program. Provide your rationale and need for training in All of Us data and acquiring these skills.
2. Provide the research question you plan to address using All of Us data and describe how you generally plan to answer the question within 4-6-months. This section should be a high-level plan that identifies the coding language (Python or R) you plan to use, your hypothesis, what analysis or statistical test you will potentially run, and a general workflow of the research project. The purpose of this section is for reviewers to gauge your understanding of the subject of interest and the potential feasibility of the project. Looking at the All of Us Data Browser can help you decide if the dataset provides the data needed to answer your research question.
3. Describe your experience with programming in Python or R, AI/ML analysis, and cloud-based computation.
4. Describe your familiarity with, and/or interest in, AI/ML analysis, programming, EHRs, clinical or genomic data analysis, biomedical science, and public health.
5. Explain how you plan to apply the training to achieve your long-term research interests and objectives.
6. Indicate whether you would like to work independently, with another trainee who has similar interests, or with a community partner on your research project. Stipends will not be provided to community partners.

Statement of Rationale for Pursuing Training: Genomics Track

Provide a personal statement of not more than 900 words. Please provide a response for each item below. The trainee selection committee will consider each of these items when reviewing your application for the Genomics Track. Include each of the following sections in your response.

1. Describe what you hope to accomplish through the Genomics Track of the AIM-AHEAD All of Us Training Program. Provide your rationale and need for training in All of Us data and acquiring these skills in genomics.
2. Provide the research question you plan to address using All of Us data and describe how you generally plan to answer the question within 4-6-months. This section should be a high-level plan that identifies genomic data you plan to use, your hypothesis, what analysis or statistical test you will potentially run, and a general workflow of the research project.
   The purpose of this section is for reviewers to gauge your understanding of the subject of interest and the potential feasibility of the project. Looking at the All of Us Data Browser can help you decide if the dataset provides the data needed to answer your research question.
3. Describe your experience with programming in Python or R and familiarity with, and/or interest in, AI/ML analysis as it relates to genomic data analysis, biomedical science, and public health.
4. Explain how you plan to apply the training to achieve your long-term research interests and objectives related to genomics.

Progress and Post-Award Reporting

Each trainee is responsible for completing milestones by the indicated due dates. There are baseline evaluations, mid-point evaluations, and post-program evaluations that are mandatory for completion. Trainees must be willing to engage in all requested reporting requirements before, during, and after the program.

A review committee composed of AIM-AHEAD Consortium members will apply the following criteria to evaluate and prioritize applications. 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 - https://grants.nih.gov/grants/guide/notice-files/NOT-OD-24-010.html

If an applicant applies to more than one training program, 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 of programs to which they applied in the application.

NOTE: Applicants not selected for the Genomics Track will still be considered for the General Track.

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 promoting a culture of diversity and inclusivity within the community.

Criteria 2

Prior Data Science Experience: Assess applicant 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 3: (Genomics Track Only)

Prior Genomics Experience: Assess applicant genomics experience by reviewing education, work history, programming proficiency, project involvement, understanding of biostatistical data analysis, and substantive knowledge of genomics.

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

• Beginner
• Intermediate 
• Expert

Applicants should expect notification of their acceptance status on November 10, 2025. Accepted applicants will receive an invite from PaymentWorks requesting:

• 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)

Trainees Will Receive

• An $8,000 stipend upon successful completion of specific trainee milestones.
• A $2,000 travel allowance to attend the 2026 AIM-AHEAD Annual Meeting.
• Support and guidance from an experienced AIM-AHEAD mentor.
• Support from the AIM-AHEAD Data Science Training Core.
• Direct 1:1 guidance, virtual office hours, help desk support, and concierge services supporting users of All of Us Researcher Workbench, R, and Python coding via RTI coaches.
• Training on:
  • Data analysis using All of Us Researcher Workbench, R, Python, and Jupyter Notebook/R Studio.
  • Use and applications of R, Python, and Jupyter Notebook/R Studio.
  • Hypothesis development for testing by analysis of All of Us data.
  • Merging and validating data across All of Us sources, building a supervised model, splitting data into subsets for model training and testing, considering anomalies that may be present and detected or missed by the model, and validating the model.
• Assistance with obtaining a Data Use and Registration Agreement (DURA) to access the Researcher Workbench, if needed, and assistance with registering for the Researcher Workbench.

Traineeship Stipend

Each trainee will receive a stipend of $8,000, which will be disbursed in three installments based on trainee completion of required milestones. Each trainee will also be provided an allowance of $2,000 to cover the cost of airfare, hotel accommodations, local transportation and per diem expenses to attend the AIM-AHEAD Annual Meeting in summer 2026. Date and location of Annual Meeting to be announced at a later date.

Trainee Mentorship

Each selected trainee will receive research and career mentorship from experienced, skilled investigators selected from AIM-AHEAD members. The online mentoring platform AIM-AHEAD Connect 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 All of Us 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 a 1 hour informational webinar on September 11 at 2 p.m. Central Time

Frequently Asked Questions

Please refer to the FAQs below before submitting a help desk ticket:

AIM-AHEAD All of Us Frequently Asked Questions

Please feel free to submit a help desk ticket if you have any questions:

AIM-AHEAD Training Programs HelpDesk

AIM-AHEAD All of Us Training Program Co-Directors

• Legend L. Burge, III, PhD, Howard University, Washington, DC
• Toufeeq Syed, MS, PhD, The University of Texas Health Science Center, Houston, TX
• Robert T. Mallet, PhD, The University of North Texas Health Science Center, Fort Worth, TX
• Megan Lewis, PhD, Research Triangle International, Research Triangle Park, NC