Graduate Research Assistant, Quantitative and Systems Health Services

University of Texas at Austin

7d•Onsite

About The Position

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.

Requirements

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.

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.