Graduate Research Assistant

Graduate Research Assistant

The LifeHD Lab conducts interdisciplinary research on social experiences, stress, health, and inequality across the life course. This Graduate Research Assistant position will support the development of data-processing pipelines for a larger study examining how the loss of family members shapes stress, health, and inequality among young adults. The position is especially well-suited for a graduate student in engineering or a related technical field with interests in wearable sensors, physiological data, signal processing, data engineering, reproducible research workflows, and human-centered health data science.

The GRA will gain experience working with intensive longitudinal biosensor and daily diary data in an interdisciplinary research setting.

The LifeHD Lab seeks a Graduate Research Assistant to help develop reproducible data-processing pipelines for high-frequency ambulatory data collected from wearable sensors worn continuously by research participants over a two-week study period. The primary device is the Empatica Embrace Plus. The GRA will support the organization, quality assessment, processing, documentation, and integration of survey data, two-week daily diary data, ambulatory sensor data, and data from a lab-based electrophysiological experiment.

This work will contribute to a larger interdisciplinary research project examining how family member loss shapes stress, health, and inequality across young adulthood.

This is a Graduate Research Assistant appointment for Summer 2026 and Fall 2026, with the possibility of continuation in Spring 2027, subject to funding, project needs, and performance. The position is supervised by Jacob

E. Cheadle, PhD, Professor of Sociology. The appointment is expected to involve 20 hours of work per week.

Responsibilities include:

• Develop and refine reproducible data-processing pipelines for high-frequency ambulatory sensor data collected from a wearable device, with primary emphasis on the Empatica Embrace Plus.
• Organize, clean, and perform quality assessment on raw and processed Empatica data, including electrodermal activity, heart rate, PPG-derived measures such as HRV, accelerometry-derived measures such as activity and sleep/wake patterns, skin temperature, and related time-stamped sensor streams.
• Develop procedures for data quality assessment, signal review, feature extraction, and preparation of analysis-ready datasets from two-week ambulatory sensor collection periods.
• Support the integration of ambulatory sensor data with survey data, two-week daily diary data, and related participant metadata.
• Develop and maintain code for data pre-processing, post-processing, and integration. MATLAB is preferred, though experience with Python or R will also be considered.
• Use Git Hub for version control and reproducible research workflows. Create clear documentation, workflow notes, and annotated code to support future use of the data-processing pipelines.
• Collaborate with research team members to troubleshoot data-processing issues, improve workflow design, and follow secure, ethical, and IRB-compliant data-handling protocols.
• As project needs develop, assist with related processing workflows for data from a lab-based electrophysiological experiment.

Required qualifications include:

• Current UT Austin graduate student in engineering or a related field.
• Programming experience in MATLAB, Python, and/or R.
• Experience working with structured time-series data and contributing to organized, reproducible data workflows.
• Ability to document code, data-processing steps, and workflow decisions clearly.
• Strong organizational skills and attention to detail.
• Ability to work independently and collaboratively as part of an interdisciplinary research team.
• Willingness to complete required training and follow lab protocols for secure, ethical, and IRB-compliant handling of human-subjects research data.

Preferred qualifications include:

• Experience with MATLAB for data processing, signal processing, scientific computing, or development of reproducible analysis workflows.
• Experience with signal processing, time-series analysis, sensor data processing, or feature…