NIH New Approach Methodologies (NAMs) and Women’s Health

Oak Ridge Institute for Science and Education

Bethesda (MD)

On-site

USD 52,000 - 65,000

Full time

14 days+

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Benefits offered by this job

Health insurance supplement
Monthly stipend

Job summary

Oak Ridge Institute for Science and Education is offering a postdoctoral research opportunity in Bethesda, MD, focusing on advancing women's health through scientific research. Participants will engage in high-priority NIH initiatives, develop proficiency in New Approach Methodologies (NAMs), and gain insights into NIH research mechanisms.

The program entails an immersive learning experience, involving collaboration across various NIH projects while honing analytical skills and communication with interdisciplinary teams. A monthly stipend is provided during the appointment.

Qualifications

  • Must be 18 years or older at the time of application.
  • Doctoral degree received within the last five years.
  • Current graduate students nearing degree completion may apply.

Responsibilities

  • Engage in STEM learning focused on New Approach Methodologies (NAMs).
  • Contribute to NIH-wide initiatives and ORWH-related activities.
  • Develop technical proficiency and enhance scientific communication skills.

Skills

Interest in women's health research
Analytical abilities
Data analysis techniques

Education

Doctoral degree in relevant fields

Job description

Description

This postdoctoral research opportunity offers an immersive STEM learning experience within the National Institutes of Health (NIH), Office of Research on Women's Health (ORWH). ORWH's mission focuses on advancing understanding of biological and social factors that impact women's health. Through this program, participants will engage in high-priority scientific initiatives that contribute to ORWH's efforts, including exploring the scientific applications of emerging NAMs (novel and alternative methodologies) in women's health.

Learning opportunities include involvement in NIH-wide research programs, such as the Computational Modeling of Hormone Homeostasis initiative, as well as collaborative efforts across NIH to examine topics such as NAMs and sex differences in health. Participants will also gain experience in the planning, coordination, and stewardship of research portfolios within these areas. Additional activities may be tailored to align with the participant's interests and skillset, providing a unique and customizable educational experience.

What will I be doing?

During this appointment, the participant will engage in a STEM learning experience focused on developing advanced technical proficiency in applying New Approach Methodologies (NAMs) to scientific questions related to women’s health and sex differences research. Through guided exploration of scientific literature, hands-on analysis, and synthesis of emerging evidence, the participant will deepen their understanding of NAMs applications that address evidence gaps and align with ORWH scientific priorities.

The fellowship provides opportunities to contribute to NIH-wide initiatives, such as the Computational Modeling of Hormone Homeostasis project, as well as other ORWH-related activities. These experiences will help the participant enhance quantitative reasoning, computational modeling, and methodological problem-solving skills. Additionally, the participant will refine their ability to communicate technical findings to interdisciplinary teams and scientific audiences, while applying these skills to explore evidence gaps within the ORWH research portfolio. This immersive experience is designed to foster growth in both technical expertise and collaborative scientific communication.

Why should I apply?

Participating in this STEM research learning program offers an opportunity to gain valuable insights into NIH research mechanisms and collaborative initiatives. Through guided exploration and mentorship, you will learn how to navigate NIH databases and apply data analysis techniques to uncover meaningful insights. You will develop skills in identifying metrics and key indicators used to evaluate research programs, comparing outcomes across different types of collaborative initiatives, and exploring data-driven approaches to inform recommendations for future programs.

This experience is designed to enhance your analytical abilities, broaden your understanding of NIH research frameworks, and provide a unique opportunity to engage with interdisciplinary scientific concepts within a collaborative learning environment.

Location

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

Stipend and Benefits

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. NIH may provide a health insurance supplement to cover the monthly premium costs if you elect the ORAU/ORISE health insurance plan, as necessary.

Appointment Length

The appointment will initially be for one year and may be renewed annually for an additional four years, contingent on the availability of funds.

Selection Timing

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.

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.

Qualifications

The qualified candidate must be 18 years or older at the time of application and should have received a Doctoral degree in one of the relevant fields and demonstrate an interest in women’s health research. The degree must have been received within the last five years of the appointment start date. Current graduate students who are nearing degree completion may apply but must have completed their degrees by the start of the fellowship.

Citizenship Requirements: This opportunity is available to U.S. citizens, Lawful Permanent Residents (LPR), and foreign nationals. Non-U.S. citizen applicants should refer to the Guidelines for Non-U.S. Citizens Details page for information about the valid immigration statuses that are acceptable for program participation.

Application Materials
  • A complete Zintellect profile.
  • A program specific application submitted in Zintellect.
  • Transcript(s) – submit the most recent official transcript. If you do not have an official transcript at the time of application, an unofficial transcript or copy can be submitted. The transcript must include the name of the academic institution, name of the student, courses completed/in progress, grades and degree expected/awarded. A copy of the official transcript and/or letter showing proof of your degree may be required prior to starting the appointment. All transcripts must be in English or include an official English translation.
  • A current resume/CV, including academic history, employment history, relevant experiences, and publication list.
  • One Recommendation – provide contact information for at least one recommendation; up to three are encouraged. Recommenders will be asked to complete a recommendation in Zintellect. Recommendations submitted via email will not be accepted. Recommendations must be submitted before your application can be reviewed.

All documents submitted must be in English or include an official English translation. All social security numbers, student identification numbers, and/or dates of birth should be removed prior to uploading.

If you have questions, contact NIHprograms@orau.org. Please include the reference code NIH-DPCPSI-ORWH-NAM-2026 for this opportunity in your email.

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