Graduate Research Assistant, Quantitative and Systems Health Services

The College of Education at The University of Texas at Austin

Graduate research assistant developing AI methods for biomedical and healthcare data at UT Austin. Building LLM, EHR, machine learning, and knowledge-graph pipelines.

Posted 8/17/2026part-timeAustin • Texas • 🇺🇸 United StatesEntry Level💰 $21,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in fine-tuning and evaluating Large Language Models and Transformer architectures for biomedical applications, alongside proficiency in Python and machine learning frameworks. Strong capabilities in data preprocessing, feature engineering, and collaborative software development are essential.

Highest-signal resume keywords

Python ProgrammingMachine LearningDeep LearningNatural Language ProcessingData Normalization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Large Language ModelsTransformer ArchitecturesData PreprocessingFeature EngineeringMissing-Data ImputationPredictive ModelingStatistical InferenceLinear AlgebraMultivariable CalculusProbabilistic Graphical Models

Soft Skills

Written CommunicationOral CommunicationCollaborative Development

Tools & Technologies

NumPyPandasScikit-LearnPyTorchTensorFlow

Industry Keywords

Biomedical InformaticsTranslational AIHealthcare DataEHR RecordsKnowledge Graphs

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

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 predictive modeling tasks in the clinical domain
Maintain open-source code repositories and write technical documentation
Prepare manuscripts for conference submission
Contribute to active research grants under the supervision of Dr. Hongfang Liu and lab faculty members
Work in the Translational AI Excellence and Application in Medicine (TEAM-AI) Lab on translating AI innovations in biomedicine and healthcare

Requirements

What you’ll need

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
Ph.D. students or advanced Master's students transitioning to doctoral studies
PhD must have been received within the last three years
Proficiency in Python and core computational libraries including NumPy, Pandas, Scikit-Learn, and 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
Track record of rigorous code documentation and collaborative software development
Relevant education and experience may be substituted as appropriate
Resume/CV and letter of interest required
Must be eligible to work in the United States and complete federal Employment Eligibility Verification (I-9) if hired
Criminal history background check required for finalists

Benefits

Comp & perks

Students in this position may choose to enroll in the UTSaver voluntary retirement programs
Criminal history background check for finalists
Equal opportunity/affirmative action employment