Call for Applications

AIM-AHEAD Bridge2AI for Clinical Care - Cohort 2

AIM-AHEAD Traineeship in Advanced Data Analysis using the Bridge2AI AI/ML for Clinical Care Network

Program Description

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 socioeconomic domains. However, many American communities, particularly those disproportionately impacted by economic and health challenges, are not receiving AI/ML’s benefits. There is an urgent need to promote equitable competencies development and capacity by enabling access to and skills for utilizing high-resolution and standardized data and software building for growing the next-generation AI scholars. Training a broadly representative workforce of researchers proficient in the application of AI/ML represents an opportunity to address a critical unmet need by extending the benefits of AI/ML to all Americans.

The overall goal of the AIM-AHEAD Bridge2AI for Clinical Care Training Program is to expand Bridge2AI for Clinical Care data access through engagement, training, and mentorship, including the use of AI/ML in big data analysis for trainees across the nation. This effort will allow AIM-AHEAD trainees to conduct novel data-driven research at the intersection of AI/ML and barriers to healthcare with a multi-modal array of data elements from a wide array of cohort participants.

To accomplish this objective, a cohort of up to 30 professionals committed to applying AI/ML to benefit American communities will complete an intensive 8-month program in advanced statistical and computational data analysis developed by the Bridge2AI team, utilizing the resources of the Bridge2AI AI/ML for Clinical Care Network and AIM-AHEAD’s Data Science Training Core. Completing the training will equip the motivated professional to conduct the in-depth analysis of large datasets essential for cutting-edge biomedical research.

Program Objectives

An important secondary outcome is that these activities will result in early trainee feedback regarding the Bridge2AI AI/ML for Clinical Care Collaborative Cloud platform environment, enabling immediate iterative improvement to maximize the platform’s wider trainee utilization and integration with other Common Fund resources.

The AIM-AHEAD consortium represented by the Data Science Training Core and Communications Hub, and Bridge2AI, including the AI/ML for Clinical Care (CHoRUS Program) and Bridge Center, are partnering to offer a data science training program to foster development of a broad workforce of researchers who are proficient in AI/ML and eager to address unmet needs in communities across America. This program is aimed to significantly accelerate the application of AI to Clinical Care through leveraging the Bridge2AI AI/ML for Clinical Care data, curriculum, tools, and train-the-trainer activities with the AIM-AHEAD expertise in trainee recruitment and development.

The AI/ML for Clinical Care Network, part of the Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI, endeavors to offer a high-resolution, responsibly sourced, AI-ready data set to respond to the challenge of improving recovery from acute illness (https://bridge2ai.org/chorus/). Drawing from 14 academic institutions, this dataset is unique in its inclusion of high-resolution waveforms, and trended EHR data of high resolution with deliberate incorporation of non-medical health factors data from geomapping, with imaging data being incorporated over time. The collaborative cloud platform enables users to access the dataset, apps, and tools for interpretable AI. Including non-medical health factors data ensures that healthcare research outcomes reflect the complex societal factors that influence health inconsistencies, providing a holistic view of patient health.

The collection and curation of data within the Bridge2AI AI/ML for Clinical Care Networks are conducted cautiously, ensuring that individual privacy is safeguarded while simultaneously capturing the complex factors that impact health outcomes. In parallel, the network has developed a series of microlearning curricula, AI/ML for Clinical Care Workshops, including hands-on modules for advanced and beginner trainees, and a mentorship network supported by train-the-trainer activities.

Using the AIM-AHEAD Connect Platform, this 8-month training program will engage a group of up to 30 graduate students, postdocs, early-career faculty, healthcare professionals, and other non-academic professionals from communities across the nation. Trainees will receive hands-on training on the Bridge2AI AI/ML for Clinical Care Network and leverage the data and tools to create practical use cases, putting their new skills to work in real-life situations and innovative data-driven research.

Training Integration and Implementation, Use Case Development, and Evaluation will include:

• Virtual live courses and workshops focused on introductory machine learning and feature engineering.
• Hands-on training on the Bridge2AI AI/ML for Clinical Care Collaborative Cloud.
• Workshops using AI/ML for Clinical Care, canonical Jupyter Notebooks.
• Introductory workshops on the OHDSI/OMOP common data model.
• Workshops and office hours on using the OHDSI tool stack for trainee projects.
• Didactics on generative AI and specific use cases to progress trainee projects.
• Trainee creation of practical use cases during Bridge2AI topics, putting new skills to work in real-life situations.
• Team projects focused on AI/ML application in clinical environment.
• Ongoing mentorship and support through the AIM-AHEAD Connect and Bridge2AI AI/ML for Clinical Care (CHoRUS) Collaborate Cloud platforms.

This robust educational framework empowers the next generation of healthcare professionals with the tools and knowledge necessary to drive the field of AI in precision clinical care focused on all communities forward. Having received advanced practical training in AI/ML Cloud Computing, AI/ML Notebooks, AI/ML Principles, and Knowledge of AI-Ready Standards, trainees completing this program will be well-prepared to harness AI/ML approaches to conduct hypothesis-driven analysis of complex datasets. The trainees will join the community of AI/ML professionals passionately committed to extending the benefits of AI/ML to all American communities. After completing the program, trainees will also have access to post-training opportunities, such as continued mentorship, collaboration within the Bridge2AI network, and ongoing research resources.

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 Bridge2AI for Clinical Care 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 serve under-resourced groups. 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

Trainees will be expected to

• Attend all training sessions, both synchronous and asynchronous, including workshops, webinars and seminars.
• Work on the program an average of at least 8 hours per week.
• Engage with an AIM-AHEAD Mentor (to be assigned through the program).
• Engage in learning communities and peer networking.
• Access the Bridge2AI AI/ML for Clinical Care (CHoRUS) data.
• Complete the provided training related to R, Python, and Jupyter Notebook, available via AIM-AHEAD Connect.
• Complete the onboarding process including an Orientation.
• Utilize Office Hours and Concierge Services provided by Tufts Team.
• Utilize AIM-AHEAD Help Desk support.
• Present a work-in-progress research poster at the AIM-AHEAD Annual Meeting in summer 2026.
• Attend and present at the annual Bridge2AI Face-to-Face Conference to be held Spring 2026 (exact dates TBD).
• Generate an abstract suitable for submission to a conference, and/or a manuscript suitable for peer-reviewed publication. The abstract and poster submitted - including the title - must comply with the US White House Executive Order.
• Utilize the Program Tracker feature in AIM-AHEAD Connect to track completion of required tasks and milestones.
• Play an active part in the AIM-AHEAD community.
• Reside in the United States (including U.S. Territories) for the duration of the program, from November 2025 to July 2026.

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  

In accordance with the goals of the AIM-AHEAD Coordinating Center, trainees from the following types of Higher Education and other institutions/organizations are highly encouraged to apply for support:

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 of non-academic institutions 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:
• 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, nurses, therapists, social workers).
• Public health (epidemiology, biostatistics, health administration, clinical implementation specialists).
7. Recommended Applicant Knowledge, Skills, and Experience:
   To ensure success in the training program, applicants must already possess certain skills and experience to optimize their learning experience and better prepare them for the challenges of the program.
   • Practical experience in coding/programming with R or Python.
   • Basic understanding of statistics.
   • A working command of English, as all training will be conducted in English.
8. Additionally, it is strongly recommended that applicants have some of the following 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.
   • Practical experience in coding/programming with R or Python.
   • Experience in data manipulation and management gained through coursework and/or research projects.

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

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 of support 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 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 or from an individual who can attest to the applicant's preparedness, aptitude, and rationale for advanced data analysis training. Letters 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

• The applicant's NIH Biosketch or CV (not to exceed 5 pages) is required.
• 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

Provide a personal statement of no more than two pages addressing the following:

• Describe what you hope to accomplish through the AIM-AHEAD Bridge 2AI for Clinical Care Training Program. Provide your rationale and need for training in AI/ML for Clinical Care data and acquiring these skills.
• Describe your familiarity with, and/or interest, in AI/ML analysis, programming, EHR, clinical or genomic data analysis, biomedical science, public health background, and/or cloud-based computation.
• Explain how you plan to apply the training to achieve your long-term research interests and objectives.

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 scientific review committee composed of AIM-AHEAD and Bridge2AI 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 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 promoting a culture of cross-disciplinary collaboration within the community.

Criteria 2:

Prior Data Science Experience: Assess the applicant’s data science experience by reviewing education, work history, programming proficiency, project involvement, and understanding of math/statistics applied in data analysis. The applicant will be assigned to one of the following categories:

• Beginner
• Intermediate
• Expert

Applicants should expect notification of their acceptance status by Monday, 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)

Trainee Benefits

Trainees Will Receive

• A stipend totaling $8,000, plus a $2,000 allowance to attend the 2026 AIM-AHEAD Annual Meeting and a $2,000 allowance to attend the Bridge2AI Face-to-Face Conference in 2026.
• 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, HelpDesk support and concierge services supporting R and Python coding and the OHDSI tool stack.
• Training on:
  • Introductory machine learning and feature engineering.
  • The Bridge2AI AI/ML for Clinical Care Collaborative Cloud.
  • Ethics and Policy issues in AI/ML.
  • AI/ML for Clinical Care canonical Jupyter Notebooks.
  • The OHDSI/OMOP common data model.
  • Generative AI and specific use cases.
  • Creating practical use cases during Bridge2AI topics.

Traineeship Stipend

Each trainee will receive a stipend of $8,000, which will be disbursed in four installments based on trainee completion of required milestones. Each trainee will also be provided a travel allowance of $2,000 to cover the cost of airfare, hotel accommodations, local transportation, and per diem to attend the 2026 Annual Meeting for AIM-AHEAD; as well as an allowance of $2,000 to cover travel expenses to attend the Bridge2AI Face-to-Face Conference.

Trainee Mentorship

Each awarded trainee will receive mentorship from experienced, skilled investigators selected from AIM-AHEAD members, who will guide the trainee in developing testable hypotheses using AI/ML for Clinical Care data. 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 Bridge2AI for Clinical Care 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 support the awardees by:

• Identifying training needs.
• Recommending customized training modules.
• Supporting HelpDesk related to data science training.

Check back for upcoming informational webinars.

Frequently Asked Questions

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

AIM-AHEAD Bridge2AI for Clinical Care Training Program FAQs

HelpDesk

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

Clinical Care Training Program HelpDesk

 AIM-AHEAD Bridge2AI for Clinical Care Training Program Directors:

• Toufeeq Syed, MS, PhD, The University of Texas Health Science Center, Houston, TX
• Eric S Rosenthal, MD, Harvard University, Boston, MA
• Nawar Shara, PhD, MedStar Health, Columbia, MD