Postdoctoral Associate - Engineering Education Research

Position Description

POSTDOCTORAL ASSOCIATE — ENGINEERING EDUCATION RESEARCH National Research Network • Quantitative Studio Pedagogy • Engineering Learning and Feedback

Applications are invited for a postdoctoral research appointment in the Meinig School of Biomedical Engineering at Cornell University. Supported by an NIH R25 award, this position offers the opportunity to help establish and lead a national, multi-institutional engineering education research collaboration examining the implementation, adaptation, and effectiveness of model-based Studio pedagogy.

The postdoctoral associate will play a central role in leading the development of the network and its shared, longitudinal research data resource, coordinating research protocols and assessment across participating institutions, and building productive collaborations with engineering faculty. The resulting dataset will support comparative investigations of how instructional practice, student engineering competencies, and learning outcomes evolve across courses, cohorts, and institutional contexts. The associate is expected to lead original analyses, first-author publications, and dissemination of findings within the engineering education research community.

This role is grounded in engineering practice as well as education research. As part of their own professional development, the associate will independently design, deliver, and refine engineering Studios and assist collaborating faculty in translating their disciplinary content into quantitative modeling, simulation, sensitivity analysis, and evidence-based engineering decision-making activities. Research will use the PRIME studio model to examine the quality of engineering reasoning and student work products, not simply participation in active learning. The associate will also contribute to the design and evaluation of computational and AI-enabled feedback systems linking classroom interactions, instructor feedback, and evolving engineering work.

The successful candidate will hold a PhD in engineering, engineering education or a closely related quantitative discipline, and bring demonstrated experience developing or applying quantitative models to substantive engineering or scientific problems, along with working programming skills for organizing and managing research data. Such experience may come from doctoral research, subsequent training, professional practice, or teaching; no specific doctoral degree title is required. Prior education research experience is valued but not essential. Candidates should demonstrate research independence, strong written communication, and an interest in collaborative, multi-site scholarship.

The position is directed by Professor Jonathan Butcher and offers mentorship in engineering education research, implementation studies, assessment, and computational feedback; opportunities for independent publications and professional networking are integral to the appointment.

Ithaca and the Finger Lakes

Cornell is located in Ithaca, New York, offering a strong interdisciplinary research environment in the beautiful and affordable Finger Lakes region, with waterfalls, trails, lakes, and a lively small city community.

To apply:

Submit a CV, cover letter describing quantitative modeling and research interests, 3 examples of work products (can be publications, course materials, etc), and contact information for 2-3 references via https://academicjobsonline.org/ajo/jobs/33097.

The hiring rate for this position will be $63,480.

Employment Assistance:

For specific questions about the position or application process, please contact the Recruiter listed in the job posting or for general questions email mycareer@cornell.edu.

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Pay Ranges:

The hiring rate of pay for the successful candidate will be determined considering the following criteria:

• Prior relevant work or industry experience.
• Education level to the extent education is relevant to the position.
• Academic Discipline (faculty pay ranges reflects 9-month annual salary)
• Unique applicable skills.