| Position Description: |
Introduction: The University of Iowa's Advanced Learning Traces (ALT) lab at the Department of Psychologicaland Quantitative Foundations has an open Postdoctoral Scholar position for an individual with expertise in computational and quantitative methods for learning research. The scholar will support grant-funded research at the ALT lab on conducting real-time affective and cognitive tracking of learners, predominantly in the context of measuring the impact of AI-driven learning technologies on learning outcomes, cognitive development, and mental health.
Details:
The ideal candidate will have methodological expertise in one of the following areas: multimodal learning analytics, with a focus on the deployment of eye-trackers or fNIRS devices; creating end-to-end prototypes of AI systems, including LLM integration, backend development, and interface design and deployment; building research software (e.g., Python packages, database systems) and other open-source tools for computational social science research. Other potentially relevant experiences and skills include knowledge of statistical and computational research methods (e.g., predictive/user modeling), cognition/affect tracking, measurement theory, analyzing high-frequency time series data, experience with ecological momentary assessment (EMA) data, and an interest in neurodivergent populations. Depending on core expertise area, the postdoctoral scholar may assist with activities related to multiple projects, including calibrating hardware and software infrastructure for research, sensor data collection and analysis; validating and disseminating research protocols and tools; creating technology prototypes and conducting user studies. Additional duties may include contributing to writing grant proposals; presenting findings at conferences and other venues; co-authoring research papers; and assisting with annual reports for grants and contracts. The scholar may supervise one or more graduate students who assist in these research-related activities. Appointment: 1-year, fiscal year appointment, with an anticipated start date in June 2026 (may be flexible). About the Alternative Learning Traces (ALT) lab:The ALT Lab focuses on studying, testing, and developing emerging EdTech, particularly those involving sensor data and artificial intelligence (AI). Current efforts focus on three priorities: 1) tracking real-time affect and cognition to understand the impact of emerging learning technologies, 2) refining and disseminating a replicable research protocol for data-driven EdTech research, and 3) supporting cross-disciplinary research projects focused on EdTech assessment and development. The University of Iowa (UI) is a world-renowned institution located in Iowa City, an UNESCO City of Literature that is recognized as one of the country's most livable communities. With more than 70,000 residents, Iowa City is an attractive haven for scholars, scientists, artists, writers, and professionals of all kinds. Highlights of living in Iowa City include outstanding schools and libraries, a lively downtown offering an array of events focused on arts, learning, and cultural development, and the unique charm of a vibrant college town connected by easy travel to larger cities like Chicago, Minneapolis, and St. Louis. Iowa City and UI are intertwined for the benefit of the university and the surrounding communities. We are committed to recruiting and retaining innovative faculty and staff, which involves providing opportunities for employees to "Build a Career and Build a Life" in the Iowa City area. The University offers several benefits to support staff and faculty in achieving a healthy work/life balance including domestic partner benefits, family caregiving leave, flexible spending accounts for dependent care and health care. For more information about local work/life resources, including dual-career support, please see: worklife.uiowa.edu The University of Iowa prohibits discrimination in employment, educational programs, and activities on the basis of race, creed, color, religion, national origin, age, sex, pregnancy (including childbirth and related conditions), disability, genetic information, status as a U.S. Veteran, service in the U.S. military, sexual orientation, or associational preferences. |