Graduate Research Assistant

University of Texas at Austin

22h • Hybrid

About The Position

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 EmbracePlus. 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.

Requirements

• 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.

Nice To Haves

• 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 extraction from high-frequency data.
• Experience working with wearable sensors, mobile health, physiological, or intensive longitudinal data.
• Familiarity with physiological data streams such as electrodermal activity, heart rate/PPG, HRV, accelerometry for sleep and activity, and skin temperature.
• Experience using GitHub or other version-control systems for collaborative or reproducible research workflows.
• Strong written communication skills, including the ability to document technical workflows clearly.
• Interest in developing work that may contribute to a thesis, publication, technical portfolio, or other professional development product.

Responsibilities

• Develop and refine reproducible data-processing pipelines for high-frequency ambulatory sensor data collected from a wearable device, with primary emphasis on the Empatica EmbracePlus.
• 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 GitHub 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.

Benefits

• UT Learn and LinkedIn Learning are free professional development programs offered to student employees
• SEED Programs under the Student Employment Office
• UTSaver voluntary retirement programs

What This Job Offers

• Job Type: Part-time
• Career Level: Entry Level
• Education Level: No Education Listed