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Assistant Research Professor, Lucy Family Institute for Data & Society
Description
The Lucy Family Institute for Data & Society at the University of Notre Dame seeks an assistant-level research professor (non tenure-track). We prefer candidates with strong expertise in advanced causal inference and computational social science methods including econometric modeling of observational data, survey design, randomized control trials and digital experiments, machine learning (ML) to construct ML-based regressors, and causal ML. The faculty member will be part of a new digital engagement for learning analytics research group within Lucy. As such, research experience with digital engagement measurement, online gaming analytics, and AI-enabled policy impact analysis will be beneficial. Given the policy-oriented learning translation analytics aspect of some of the research, ideal candidates will also have a robust track record of teaching excellence demonstrated through multiple years and sections of lead-instructor course delivery (with high teaching evaluations). The research faculty will be working closely with Professors Ahmed Abbasi (Director of the Institute), Rick Johnson (Associate Director of the Institute), and Sugana Chawla (Data Science Education Program Director).
The ideal candidate would have evidence of excellence in research and scholarship. The ideal candidate would have also demonstrated an interest in interdisciplinary work, as evidenced through projects or research publications.
Expectations
- Help establish a research program for robust measurement and causal inference in digital settings including but not limited to online video games.
- Publish in top venues, and/or pipeline evidence such as revise-and-resubmits at top academic journals (e.g., UTD-24, Economics, Science/Nature/PNAS, etc.);
- Pursue interdisciplinary research by building collaborations;
- Mentor or co-mentor graduate and undergraduate students;
- Lead and collaborate on research grants;
- Teach / co-teach courses on related topics;
Qualifications
- Requires a PhD in with methodological expertise in causal inference via econometric modeling, machine learning, analysis of digital trace and survey data, and experiment design.
- Research experience (projects, papers) related to digital engagement, online gaming telemetry data, and analysis of policy impact and implications, with a track record of experience beyond doctoral studies in the domain.
- Strong demonstrated teaching skills.
Application Instructions
Please submit a CV, a research statement, teaching statement, and three confidential letters of recommendation via Interfolio.