Graduate Research Assistant, Quantitative and Systems Health Services

About this role

Job Posting Title: Graduate Research Assistant, Quantitative and Systems Health Services ---- Hiring Department: Quantitative and Systems Health Science (QSHS) ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 20 ---- FLSA Status: Exempt from FLSA ---- Earliest Start Date: Aug 24, 2026 ---- Position Duration: Expected to Continue Until Dec 31, 2026 ---- Location: UT MAIN CAMPUS ---- Job Details: Purpose The TEAM-AI Lab invites applications from Ph.D. students (or advanced Master's students transitioning to doctoral studies) in Computer Science, Biomedical Informatics, Data Science, or Engineering to join the lab as Graduate Research Assistants.   The GRA role provides advanced doctoral training at the intersection of artificial intelligence, healthcare data science, biomedical discovery, and clinical translation. Working under the supervision of Dr. Hongfang Liu and lab faculty members, GRAs contribute to the execution of active research grants. PhD must have been received within the last three years.

The applicants will join a collaborative research environment at the Translational AI Excellence and Application in Medicine (TEAM-AI) Lab, focusing on accelerating the translation of AI innovations in biomedicine and healthcare. The lab consists of faculty members, program managers/coordinators, data scientists, and scientific programmers. The activities carried out by the team range from advancing AI innovations through big data, empowering biomedical and clinical sciences through team science collaboration and best practices, to building human-centered, value-added, and evidence-based tools, resources, and services to facilitate real-world implementation of said innovations. 

Responsibilities • Fine-tune, prompt-engineer, and evaluate open-source Large Language Models (LLMs) and Transformer architectures for biomedical data normalization.

• Map observational healthcare data to data standards and assist in constructing common data elements and knowledge graphs for disease areas.

• Develop data-preprocessing, feature-engineering, and missing-data imputation pipelines for longitudinal EHR records, time-series vitals, and diagnostic imaging features.

• Implement and benchmark baseline machine learning algorithms for various predictive modeling tasks in the clinical domain.

• Maintain open-source code repositories, write technical documentation, and prepare manuscripts for conference submission.

Required Qualifications • Enrolled in a Ph.D. program at The University of Texas at Austin in Computer Science, Biomedical Informatics, Data Science, Electrical & Computer Engineering, or a related quantitative field.

• Proficiency in Python and core computational libraries (NumPy, Pandas, Scikit-Learn, PyTorch/TensorFlow).

• Coursework or experience in machine learning, deep learning, natural language processing, or probabilistic graphical models.

• Solid background in linear algebra, multivariable calculus, probability theory, and statistical inference.

• Written and oral communication skills, with a track record of rigorous code documentation and collaborative software development.

Relevant education and experience may be substituted as appropriate.

Salary Range $41,600 ($21,800 prorated for .5 FTE (20 hours a week))

Working Conditions • May work around standard office conditions

• Repetitive use of a keyboard at a workstation

• Use of manual dexterity

• Occasional weekend, overtime and evening work to meet deadlines

Required Materials • Resume/CV

• Letter of interest

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

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