Postdoc, AI Health Innovation & Data Science

University of Texas at Austin; Austin (TX); On-site; USD 63,000 - 90,000; Full time

Job summary

University of Texas at Austin in Austin, TX invites applications for a Postdoctoral Fellow in the TEAM-AI Lab within the Department of Quantitative and Systems Health Sciences. The role focuses on methodology and software development at the intersection of health data science, multimodal AI, digital twins, and clinical informatics.

The successful candidate will design novel algorithms, coordinate multi-institutional research networks, and translate real-world health data into actionable clinical

Qualifications

• PhD in a quantitative field earned within the past three years.
• Strong background in mixture-of-experts, transformers, and RL for biomedical data.
• Experience with multimodal NLP, LLMs, and causal inference is preferred.
• Familiarity with high-performance computing and large clinical datasets.

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 NLP, imaging features, and non-medical determinants.
• Coordinate AI and computational phenotyping work streams within the national 10-institution ReCARDOnetwork to extract and standardize CDEs for Alzheimer’s research.
• Construct deep language models and clinical NLP pipelines to extract phenotypes, biomarkers, and treatment responses from progress notes for the WONDER project.
• Engineer semantic knowledge graphs and database query architectures for perioperative pathophysiological mechanisms in organ injury research.
• Authorship of high-impact first-author or co-author manuscripts in leading informatics and ML journals and conferences.

Skills

Mixture-of-experts architectures

Reinforcement learning

Multimodal NLP & Fusion

Regulatory science & Explainable AI

High-performance computing

Clinical data science

Education

Ph.D. in Biomedical Informatics, Computer Science, Data Science, Electrical & Computer Engineering, Applied Mathematics or related quantitative field earned within the past three years

PhD earned within the last three years

Job description

University of Texas at Austin in Austin, TX invites applications for a Postdoctoral Fellow in the TEAM-AI Lab within the Department of Quantitative and Systems Health Sciences. The role focuses on methodology and software development at the intersection of health data science, multimodal AI, digital twins, and clinical informatics.

The successful candidate will design novel algorithms, coordinate multi-institutional research networks, and translate real-world health data into actionable clinical