Postdoctoral Fellow, TEAM-AI Lab, Department of Quantitative and Systems Health Sciences

Dell Medical School is seeking a Postdoctoral Fellow for the TEAM-AI Lab within the Department of Quantitative and Systems Health Sciences. This is a full-time, exempt position with an expected duration until August 31, 2027, and may be renewable based on funding and performance.

About the role

The TEAM-AI Lab seeks multiple Postdoctoral Research Associates to lead methodological innovation, software architecture engineering, and scientific execution across its active grant portfolio. Working under the direct mentorship of Dr. Hongfang Liu and lab faculty, the Postdoctoral Researcher will drive research at the intersection of health data science, multimodal AI, digital twins, computational phenotyping, and responsible AI.

This position provides structured preparation for an academic tenure-track career or a lead research scientist role in industrial AI labs, offering access to national data networks, high-performance computing clusters, and clinical interdisciplinary collaborations across UT Austin.

The successful candidate 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.

Responsibilities

• Lead the design and implementation of mixture-of-experts neural architectures and reinforcement learning pipelines for counterfactual disease trajectory simulation for EMED, an NIH-funded multi-modal AI project.
• Architect and evaluate multi-site cardiotoxicity risk prediction models integrating structured EHRs, clinical notes via natural language processing, strain echocardiography features, and non-medical determinants of health under the FDA CardioOnco-AI award.
• Coordinate AI and computational phenotyping work streams within the national 10-institution ReCARDO network to extract, standardize, and validate Common Data Elements (CDEs) for Alzheimer's disease research.
• Construct deep language models and clinical natural language processing pipelines to extract structured oncologic phenotypes, molecular biomarkers, and treatment responses from progress notes for the WONDER project.
• Engineer semantic knowledge graphs and database query architectures capturing perioperative pathophysiological mechanisms for acute organ injury research under POI-KB.
• Authorship of high-impact first-author or co-author manuscripts in leading informatics and machine learning journals and conferences.
• Other related duties as assigned.

Requirements

• Ph.D. in Biomedical Informatics, Computer Science, Data Science, Electrical & Computer Engineering, Applied Mathematics, or a related quantitative field earned within the past three years.
• Strong background in one or more of the following areas:
  • Generative and trajectory modeling including transformers, mixture-of-experts, reinforcement learning, simulation, and counterfactual analysis.
  • Multimodal NLP & Fusion, large language models (LLMs), cross-attention fusion, vision-language transformers.
  • Ontological engineering, knowledge graph construction and mining, CDE development for data harmonization.
  • Regulatory science and explainable AI, verification, validation, uncertainty quantification, and AI evaluation frameworks.
  • High-performance computing and big data analytics.

Qualifications

• Demonstrated understanding of model validation, clinical trial design, and causal inference techniques.
• Experience with transformer-based models, LLMs, retrieval-augmented generation (RAG), or foundation models.
• Experience analyzing complex real-world clinical datasets (e.g., MIMIC-IV, OMOP CDM, PCORnet, NACC Uniform Data Set, or state cancer registries).
• Knowledge of causal inference, clinical prediction modeling, or multimodal AI.
• Experience with responsible AI, model evaluation, fairness, privacy, or explainable AI.
• Experience with biomedical image processing, radiomics, or digital pathology.
• Scientific programming on Linux or high-performance computing environments.

Active Grant Portfolio

• EMED: An Ethical Mixture-of-Experts Digital Twin Framework for Medical Device Surveillance (NIH Project Details)
• CardioOnco-AI: AI-Empowered Cardiotoxicity Risk Prediction Among Breast Cancer Survivors Using Multi-Site Real-World Data (FDA Details)
• ReCARDO: Using Real-World Data to Derive Common Data Elements for Alzheimer's Disease and AD-Related Dementias Research Through Ontological Innovation (NIH Project Details)
• WONDER: Accelerating Real World Data-driven Precision Oncology through Data Science and Informatics Excellence in Research (CPRIT Details)
• POI-KB: Design and Development of a Knowledgebase for Accelerating Perioperative Organ Injury Research and Translation (NIH Project Details)

Benefits

This position is eligible for the Teacher Retirement System of Texas (TRS) or the Optional Retirement Program (ORP) if working 40 hours per week for at least 135 days.

Pay

Salary starts at $63,480+ depending on NIH Level.

Schedule

• Full-time, 40 hours per week.
• May require occasional evening, weekend, or travel commitments for research activities, conferences, and collaborative projects.

Working Environment

• Standard office equipment and repetitive use of a keyboard.
• May be exposed to occupational hazards such as communicable diseases, bloodborne pathogens, ionizing and non-ionizing radiation, hazardous medications, and disoriented or combative patients.
• May work in research laboratories, clinical environments, hospitals, ambulatory settings, or field research locations.
• May handle biological specimens, chemicals, hazardous materials, or laboratory equipment consistent with assigned research activities.
• May periodically lift and move research materials and equipment in accordance with organizational safety requirements.
• Requires visual acuity and manual dexterity sufficient to operate research equipment and computer systems.