The IES-PIRT Program is accepting new applications to become a PIRT Affiliate
NYU’s Institute of Education Sciences-funded Predoctoral Interdisciplinary Research Training (IES-PIRT) program is committed to strengthening the pipeline of skilled PhD researchers prepared to conduct rigorous education research relevant to education in the United States.
We are now accepting applications to be selected as an Affiliate of NYU’s IES-funded Predoctoral Interdisciplinary Research Training. Directed by Elise Cappella and Michael Kieffer, with a dynamic faculty leadership team, this program offers a unique environment for multidisciplinary training and community-building across NYU’s schools. The application is open to current PhD students working in education science across varied disciplines and departments in New York City. Both early stage PhD students who are solidifying their research interests and more advanced students who are actively engaged in dissertation work are encouraged to apply.
For 2026-27, NYU PIRT Affiliates will receive a stipend of $3,000 along with ample opportunities to develop new methodological skills, integrate insights from a range of fields into their research, generate innovative project ideas, and get exposure to the larger education research community. Affiliates will meet and network with leading national scholars from universities and other employers, work together with a diverse and vibrant group of peers, and learn new professional skills relevant to careers in academia and beyond.
NYU PIRT Affiliates will be expected to participate in weekly seminars, present their research, and attend the annual IES-PIRT Conference in the spring semester. PIRT Affiliates will have additional opportunities to engage in workshops on methodological and professional topics.
To be eligible for the program, candidates must meet the following requirements:
- Be a U.S. citizen or permanent resident (a requirement of the funder)
- Exhibit an interest and/or commitment to research in education relevant to the U.S. context
- Demonstrate aptitude for and interest in quantitative methods, mixed methods and/or data science
- Commit to completing a dissertation on a topic relevant to U.S. education (dissertation topics are subject to IES review)
- Matriculated as a full-time PhD student
- Mentorship from an NYU faculty member with research relevant to U.S. education
- Available every Monday during the academic year from 12:00 to 1:40pm in person (NYC).
To apply for this program, please submit the following materials as a single PDF to ies.pirt.admin@nyu.edu by July 1, 2026:
- Curriculum Vitae
- Unofficial Transcript
- Personal Statement (500 words): Describe how you meet the eligibility requirements above, your specific interests in the education sciences, and your experience and interest in quantitative methods, mixed methods, and/or data science.
- Other Information (50 words): Please let us know your program, your year in the program, if you are beyond your fellowship years, and any other sources of stipends or funding you have. Provide contact information for a faculty mentor who can provide a recommendation upon request.
Please share this opportunity with any NYU doctoral students who may be interested. Questions about the program or your eligibility can be sent to ies.pirt.admin@nyu.edu. We look forward to receiving your application!
Learn more about IES-PIRT:
IES-PIRT Affiliated Faculty
A wide and talented array of interdisciplinary faculty serve as IES-PIRT mentors and advisors.
IES-PIRT
For over a decade, IHDSC and faculty from seven NYU academic units have trained incoming and advanced doctoral students from diverse backgrounds to become outstanding researchers in the educational sciences.
Proseminar Series
In addition to funding doctoral students from the seven affiliated departments across NYU the program includes a proseminar series. The series brings together presentations by both NYU and external experts who will help to introduce, reintroduce, and consolidate students' advanced understanding of the concepts of internal, external, construct, and statistical validity.
