Research Assistant Machine Learning Jobs in Georgetown, TX

Job Posting Title:
Graduate Research Assistant
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Hiring Department:
Population Research Center
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Position Open To:
All Applicants
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Weekly Scheduled Hours:
20
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FLSA Status:
Exempt from FLSA
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Earliest Start Date:
Immediately
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Position Duration:
Expected to Continue Until Aug 31, 2027
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Location:
UT MAIN CAMPUS
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Job Details:
General Notes
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.
What benefits do I receive with UT? As a student employee of the university, you may be eligible for certain kinds of benefits. For more details on benefits, please see: https://hr.utexas.edu/student/student-employee-insurance-benefits.
UT Learn and LinkedIn Learning are free professional development programs offered to student employees, as well as the SEED Programs under the Student Employment Office:
https://hr.utexas.edu/learning-development
https://www.linkedin.com/learning/?u=36306084
https://hr.utexas.edu/student/SEED
https://hr.utexas.edu/student/SEED/seed-workshops
For more information about the College of Liberal Arts, please visit: https://liberalarts.utexas.edu/.
Purpose
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.
Appointment Information
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

• 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.

Required Qualifications

• 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

• 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.

Salary Range
$35,440 annual rate
Working Conditions
Work will be computer-based, with substantial scheduling flexibility. Some tasks may require work in the lab. The position will involve working with confidential human-subjects research data and following secure, ethical, and IRB-compliant data-handling protocols.
Work Shift
20 hours of work per week. The schedule will be arranged in coordination with the supervisor, with flexibility for coursework and project needs.
Required Materials

• Resume/CV
• Letter of interest: In a ~1-page letter of interest, applicants should briefly describe their relevant programming experience, experience with time-series or sensor data, interest in the position, and any prior work developing reproducible data-processing workflows.

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes.
Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.
Employment Eligibility:
Please confirm your eligibility for this position here: http://www.utexas.edu/hr/student/student_acad_employment.html
Retirement Plan Eligibility:
Students in this position may choose to enroll in the UTSaver voluntary retirement programs.
Background Checks:
A criminal history background check will be required for finalist(s) under consideration for this position.
Equal Opportunity Employer:
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
Pay Transparency:
The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.
Employment Eligibility Verification:
If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university.
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E-Verify:
The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university's company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:

• E-Verify Poster (English and Spanish) [PDF]
• Right to Work Poster (English) [PDF]
• Right to Work Poster (Spanish) [PDF]

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Compliance:
Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.
The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.