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NIH Post-baccalaureate Fellowship Clinical Standards and Informatics and Operations

Zintellect
Bethesda, MD Intern
POSTED ON 6/13/2026
AVAILABLE BEFORE 8/13/2026

STEM Learning Fellowship in Healthcare Data Analysis at NIH ODSS

The National Institutes of Health (NIH) Office of Data Science Strategy (ODSS) is seeking recent Post-Baccalaureates to apply to a STEM-focused hands-on learning experience centered on healthcare data analysis and modeling. ODSS leads the implementation of the NIH Strategic Plan for Data Science through collaboration with NIH institutes, centers, and offices. This fellowship offers participants an opportunity to gain practical experience in developing data standards, launching research initiatives, and exploring the role of clinical informatics in biomedical research. Candidates with foundational knowledge in computer science (including advanced R/Python programming and AI/ML techniques), clinical informatics, and strong organizational skills are encouraged to apply.

What Will I Be Doing?

Through this immersive learning opportunity, participants will:

  • Develop foundational knowledge in clinical informatics, data access control, data standards, and interoperability concepts relevant to NIH programs.
  • Build skills in program analysis, project coordination, and tracking activities across clinical informatics and data science initiatives.
  • Conduct environmental scans and literature reviews, synthesizing findings into reports, presentations, and briefing materials.
  • Develop skills in stakeholder engagement through interaction and communication across multidisciplinary teams.
  • Strengthen understanding of how clinical, research, and operational data are used to inform biomedical research strategies and programmatic activities.

By the end of the fellowship, participants will have gained and understanding of how NIH maintains documentation for reproducibility, manages standard operating procedures (SOPs), and coordinates data collected in alignment with agency policies and procedures.

Why Should I Apply?

This STEM learning fellowship provides a unique opportunity to deepen your expertise in healthcare data analysis and modeling while exploring NIH-led initiatives in clinical informatics and data science. Participants will refine their analytical and organizational skills, engage in interdisciplinary collaboration, and gain insights into the practical application of data standards and interoperability concepts. Guided by experienced mentors, participants will contribute to meaningful projects that enhance their professional development and scientific knowledge.

Where will I be located?

Fellows are expected to be fully engaged, either in-person or remote. In-person location is Bethesda, MD.

What financial provisions will I receive?

The selected candidates will receive a monthly stipend to help offset living and other expenses during this appointment. Stipend rates are determined by NIH officials and are based on the candidate’s academic and professional background. In addition, NIH may provide a health insurance supplement to cover the monthly premium costs if you elect the ORAU/ORISE health insurance plan, as necessary.

What is the length of the appointment?

The appointment will initially be for one year and may be renewed annually up to four additional years upon recommendation of NIH and is contingent on the availability of funds. 

When are selections made?

An initial review of applications will occur on July 1, 2026. Thereafter, applications will be reviewed on a rolling-basis throughout the 2026 calendar year, and selections made as projects for participation become available.

What is the Nature of the Appointment?

This program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and the National Institutes of Health (NIH). Participants do not become employees of NIH, DOE, ORISE, nor ORAU, and there are no employment-related benefits.

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