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NYU Postdoctoral Associate in Education

Employer
New York University
Location
Postdoc out of New York University; fully remote work option is available
Salary
$60,000 - $70,000
Date posted
Apr 18, 2023

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Position Type
Faculty Positions, Education, Special Education
Employment Level
Post-Doc
Employment Type
Full Time

Location:
New York University; Ability to work remotely if desired
Open Date:
To begin in or near September 2023 (start date flexible)
Salary:
$60,000-$70,000

Description:
Drs. Rachel Fish and Kenneth Shores at New York University and the University of Delaware are seeking
applications for a one-year post-doctoral fellow in education with quantitative experience analyzing
administrative and nationally representative datasets.

The one-year fellowship is a research fellowship, whereby the Postdoctoral Fellow will work closely with
Drs. Fish and Shores. The purpose of this research is to establish the robustness and generalizability of
access to special education services among racially and ethnically minoritized student populations.

The one-year fellowship is funded by grants from the William T Grant and Spencer Foundations. We
welcome recent PhDs or EdDs in education or related fields where the candidate has pursued
quantitative research using administrative or other secondary datasets. Compelling applicants will have
an established research record demonstrating a commitment to addressing inequities in schools,
particularly by race and/or disability.

Please note that any accepted candidate will be able to work remotely if desired.

Essential Functions:

  • Build datasets from multiple sources, including administrative data from state and district partners as well as nationally representative datasets from the National Center for Education Statistics (NCES).
  • Work with Drs. Fish and Shores to establish data sharing agreements (DSAs) and secure data storage practices to house administrative data.
  • Conduct statistical analysis to quantify differences in special education placement rates across datasets and contexts.
  • Apply statistical approaches like robust meta-analysis and matching estimators to characterize robustness and variability of these results.
  • Manuscript preparation for peer review publication and conference presentation.

Required Education, Knowledge, Skills, Abilities:

  • Ph.D. or Ed.D. in education or related fields.
  • 0-2 years experience (recent Ph.D. graduates encouraged)
  • Excellent writing, research, and communication skills.
  • Excellent organizational and interpersonal skills.
  • Experience with statistical modeling and data management.
  • Demonstrated ability to:
    • Work independently with minimal supervision.
    • Set priorities and effectively manage time and resources.
    • Be a constructive and cooperative member of a team.
    • Positively influence colleagues and develop collaborative relationships with a wide range of people.

Note:
This position profile identifies the key responsibilities and expectations for performance. It cannot
encompass all specific job tasks that an employee may be required to perform. Employees are required
to follow any other job-related instructions and perform job-related duties as may be reasonably
assigned by his/her supervisor.

Application Instructions:
For full consideration, please send a cover letter addressed to Drs. Fish and Shores, CV/resume and
contact information for 3 references. At least two confidential letters of recommendation will be
requested for eligible candidates. No recommendation letter is needed for the initial application. The
position will begin in or near September 2023, with flexibility on the start date. Applications arriving by
June 30, 2023 will be given priority consideration.

For additional information, please contact Dr. Rachel Fish at Rachel.fish@nyu.edu or Kenneth Shores at
kshores@udel.edu.

New York University is an AA/EO employer and values an inclusive and diverse learning community and
campus climate. All qualified applicants will receive consideration for employment without regard to
race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran
status, or any other characteristic protected by law.

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