Call for Applications

AIM-AHEAD & NCATS Training Program

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

Program Timeline

Program Description

The application of artificial intelligence and machine learning (AI/ML) to large health datasets is dramatically expanding the capacity for hypothesis testing impacting the biomedical and socioeconomic domains. The central goal of this training program is to increase researcher diversity in AI/ML by training individuals from diverse backgrounds who are committed to gaining proficiency in using AI/ML for health data analysis and applying their expertise to benefit communities underrepresented in biomedical research.  

To accomplish this objective, diverse professionals committed to applying AI/ML to benefit underrepresented communities will complete an introduction to health data analysis training program developed to equip the motivated professional to conduct the analysis of large datasets essential for cutting-edge biomedical and socioeconomic research.

View the AIM-AHEAD & NCATS Training Program Infographic linked below which offers a visual overview of the first cohort's academic achievements, acquired competencies, and substantive evaluations provided by trainees regarding their personal experiences throughout this extensive training program.

AIM-AHEAD & NCATS Training Program Infographic.

Traineeship Objectives

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, health care, and health equity:

The AIM-AHEAD consortium and the National Center for Advancing Translational Sciences (NCATS) have partnered to offer this unique training opportunity designed to increase researcher diversity in AI/ML by reducing barriers to accessing, training on, and analyzing real-world clinical data. This program enables participants to conduct novel research at the intersection of AI/ML and health disparities, with data collected from underrepresented communities in biomedical research.

Trainees will complete online courses, participate in live classes, and learn hands-on skills conducting a project using the NCATS National COVID 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 like COVID-19 and supports equitable access and innovative research into treatments, health care practices, and improving health equity. Through this program, participants will learn to ethically and effectively apply AI/ML methods to this robust data, unlocking insights that can address health disparities and improve health outcomes for traditionally underserved populations.

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, Approaches & Applications (I, II, and III), and Presentations.

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

Approaches & Applications I, II, and III consist of three online courses where trainees delve into the core approaches for using AI/ML in health care, including AI/ML models, clinical data ontologies, good algorithmic practices, causal inference, and health equity. In parallel, trainees are guided in applying these skills in team projects using the N3C COVID Enclave data. These projects focus on health issues such as COVID-19, cancer, and cardiometabolic health, with the support of clinical advisors and program instructors.

The final Presentations phase includes training in appropriately interpreting and communicating results. All trainees are provided the opportunity to present at the AIM-AHEAD Annual Meeting 2025. This program prepares participants to become proficient in AI/ML research, specifically geared toward advancing health equity by tackling real-world challenges and contributing to impactful, ethical 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.

Program Format, Expectations and Benefits

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. Wednesdays 12:00 - 1:30 p.m. 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 team project activities
  • Meet weekly with their project teams
  • Meet monthly with AIM-AHEAD Career Mentors
  • Work independently to ensure on time completion of self-paced online courses
  • Work professionally, respectfully, and collaboratively within a team of peers
  • Proactively communicate and utilize N3C and AIM-AHEAD HelpDesk support
  • 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 2025
  • Engage professionally and proactively communicate with all NCATS and AIM-AHEAD instructors, staff, mentors, and advisors
  • 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

National Clinical Cohort Collaboration (N3C) Data Accounts:

N3C maintains high security and privacy guidelines. All training in this program is done within the N3C platform (no data may be removed) and with synthetic or de-identified data (Level 2). Note: Trainees do not view any Protected Health Information. 

All accepted trainees must register for an N3C account in order to access the data sets used for the courses. Trainees complete their Foundations courses in the Education Enclave (Dec - Jan) and then progress to the COVID Enclave for the Approaches and Applications courses and team projects (Jan - Aug). 

Trainees are required to register within 30 days of the Notice of Award. Given that some institutions may close for winter break, we strongly encourage all applicants to confirm or begin registration right away, using the instructions below. If a trainee is not able to complete registration before the Approaches & Applications courses begin, the trainee may not be able to continue in the program, and stipends will not be issued.

To Register: 

1. Confirm your Institution is listed on the N3C DUA Signatories List. 
2. If your organization is listed, follow the instructions to register.
3. If your organization is not currently listed, download the DUA form and ask your institutions’ signing official to sign and email it to NCATSPartnerships@mail.nih.gov. About 1 week after they email it, you should see your institution on the DUA Signatories List, and can proceed with registration.  

If you need assistance while registering, visit https://covid.cd2h.org/support/ to join the N3C Office Hours or submit a help ticket. 

Trainees Will Receive

• • A $8,000 stipend based upon successful completion of the trainee requirements
  • A $2,000 allowance to attend the AIM-AHEAD Annual Meeting 2025
  • Access to the N3C, including the Educational Enclave, COVID Enclave, HelpDesk, and all N3C 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
  • Technical support and office hours from the N3C support team
  • Professional support and guidance from an experienced career mentor
  • Clinical guidance from a clinical project advisor

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 AIM-AHEAD Annual Meeting 2025.* Date and location of Annual Meeting to be announced at a later date.

*Trainees may apply to multiple AIM-AHEAD training programs and fellowships, but if accepted into more than one, they must select only one AIM-AHEAD training program to participate in per funding cycle. Additionally, AIM-AHEAD-funded Coordinating Center Personnel (MPIs, PIs, Co-Investigators, other personnel) are eligible to apply, but if accepted, they will not receive the stipend and allowance.*

Trainee Mentorship

Each awarded trainee will receive research and career mentorship from experienced, skilled investigators selected from AIM-AHEAD core 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.

1. Applicants must be:
   1. U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals
      • US Citizen: An individual who is a citizen of the United States by law, birth or naturalization (https://www.law.cornell.edu/definitions/uscode.php?width=840&height=800&iframe=true&def_id=42-USC-630966247-802284531&term_occur=1&term_src=title:42:chapter:99:section:9102)
      • Permanent Resident: An immigrant/non-citizen who can legally reside in the United States in perpetuity (https://www.law.cornell.edu/wex/lawful_permanent_resident_(lpr))
      • Non-Citizen National: A person born in an outlying possession of the United States on or after the date of formal acquisition by the United States (https://www.law.cornell.edu/uscode/text/8/1408)
   2. Able to submit Form W-9 (Request for Taxpayer Identification)
   3. Affiliated with one of the following entities:
      • Higher Education Institutions
        • Public/State Controlled Institutions of Higher Education8
        • Private Institutions of Higher Education
      Individuals affiliated with the following types of Higher Education Institutions are always encouraged to apply:
      • Hispanic-serving Institution
      • Historically Black Colleges and Universities (HBCUs)
      • Tribally Controlled Colleges and Universities (TCCUs)
      • Alaska Native and Native Hawaiian Serving Institutions
      • Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)
   4. Nonprofits Other Than Institutions of Higher Education
      • Nonprofits with 501(c)(3) IRS Status (Other than Institutions of Higher Education)
      • Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)
   5. For-Profit Organizations
      • Small Businesses
      • For-Profit Organizations (Other than Small Businesses)
2. 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 of non-academic institutions as defined in item 1 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:
   • 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)
3. Applicant Preparation and Experience

   This training opportunity is open to applicants with the following experience:

   1. Applicants possessing a clinical background with limited exposure or experience with data science, statistics, or programming (e.g., Python or R).
   2. Applicants who are proficient in data science, statistics, or programming (e.g., Python or R) with limited exposure or experience with clinical medicine, public health, or ethics training related to clinical data.

   Although it is not mandatory, it is strongly recommended that applicants have some of the following foundational skills and experiences to optimize their learning experience and better prepare them for the challenges of the program:

   • Successfully completed an undergraduate or graduate course in probability and statistics
   • Has practical experience in coding/programming with R or Python
   • Has experience in data manipulation and management gained through coursework and/or research projects
   • Has experience with health research, public health, or health equity
   • Has experience working in multidisciplinary or interprofessional teams

   Introductory or refresher foundational courses on these topics will be available to successful applicants at the start of the traineeship.

Applications must be submitted within the designated application period. Applicants should ensure that their submissions address the criteria outlined below, as well as any additional questions provided in the Traineeship Application Form. The application should be written clearly, in a way that is accessible to individuals outside the applicant's specific field of study. The applicant must provide a statement of rationale for pursuing training including interest and/or prior skills in AI/ML for healthcare, potential research topics, and the plan for engaging with AIM-AHEAD (see full description below).

Applicants must :

1. 1. Register as a “mentee/learner” on AIM-AHEAD Connect (our Community Building platform). Your AIM-AHEAD Connect profile will allow you to log in to submit your application on the InfoReady platform
   2. Click here to submit an application for review using the InfoReady platform

Please use Chrome, Firefox, or Edge. Please note that both of the above steps must be completed for consideration.

All applications must be received by November 18, 2024, at 11:59 p.m. Eastern Time.

Profile Information

• • Name, organization, department, position title, location during the training year (December 2024 to August 2025), area of interest, email address, and profile web page, if any
  • Confirmation of whether their institution currently has or needs a signed N3C Institutional Data Use Agreement on file (see https://covid.cd2h.org/duas/)
  • Please answer all additional profile questions in InfoReady

Letters of Support

• • One signed letter of support from the applicant’s supervisor is required. Letters of support must include the referee’s contact information (full name, position title, organization, email/phone number, and signature)
  • Minimum of one letter of recommendation (one additional letter may be provided) 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. Reference(s) should highlight relevant skills and accomplishments

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 fellow

The applicant's NIH biosketch or Curriculum Vitae (CV) is required and should not exceed 5 pages. Please prepare the Fellowship Biosketch

• • NIH biosketch form: https://grants.nih.gov/grants/forms/biosketch.htm
    • Template: Traineeship Biosketch
    • Instructions for a Biosketch
    • 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, health care, and health equity. 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, health equity, and 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.
  • Describe 1 - 2 topic areas or research questions you would like to study using the N3C Data Enclave. Note: As a federal requirement, all studies using the N3C COVID Enclave must be for the benefit of improving COVID care and can also explore other conditions (e.g., which COVID treatments were most effective for people with diabetes? Do people with lung cancer have higher rates of long COVID?). A list of N3C projects and publications may be found at: https://covid.cd2h.org/projects/. 
  • Lastly, explain how, if accepted, you plan to apply the training and project experience to achieve your long-term research interests and career goals.

A Review Committee comprised of AIM-AHEAD Consortium 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).

Criteria 1: 

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 2 will be applied to uphold equitable balance across the program and during team formation, ensuring a diverse mix of trainees across all skill levels. This approach fosters inclusivity and ensures that opportunities are accessible to individuals with varying degrees of experience, contributing to a well-rounded cohort.

Applicants should expect to be notified of their acceptance status by Tuesday, December 17, 2024. Accepted applicants will receive an invite from PaymentWorks requesting:

• a valid tax ID (either an EIN or SSN) via W9 for US 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)

Accepted applicants will have 3 business days to accept or decline the traineeship offer. Individuals who accept the offer will then begin immediately with the onboarding process, including creating their N3C Data Enclave accounts, setting up AIM-AHEAD Connect accounts if not already registered, etc. Accepted applicants should be prepared to expedite submission of their banking information to the University of North Texas Health Science Center to receive payments.

More details to come

AIM-AHEAD & NCATS Training Program Co-Directors

Robert Mallet, PhD, Legand (Lee) Burge, PhD, Toufeeq Syed, PhD

Frequently Asked Questions

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

AIM-AHEAD & NCATS Frequently Asked Questions

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

AIM-AHEAD Training Program HelpDesk