Postdoctoral Fellow, AI Healthcare Innovation

The Translational AI Excellence and Application in Medicine (TEAM-AI) Lab in the Department of Quantitative and Systems Health Sciences at Dell Medical School, The University of Texas at Austin, seeks postdoctoral fellows to lead methodological innovation, software architecture and scientific execution across its grant portfolio under the mentorship of Dr. Hongfang Liu. Research spans health data science, multimodal AI, digital twins, computational phenotyping and responsible AI: mixture-of-experts architectures and reinforcement learning for counterfactual disease-trajectory simulation in medical device surveillance (NIH-funded EMED); multi-site cardiotoxicity risk prediction for breast cancer survivors that integrates structured EHRs, clinical notes, strain echocardiography and non-medical determinants of health (FDA CardioOnco-AI); computational phenotyping and common data elements for Alzheimer's disease research across the 10-institution ReCARDO network; clinical NLP pipelines extracting oncologic phenotypes, biomarkers and treatment responses for real-world precision oncology (WONDER); and semantic knowledge graphs of perioperative pathophysiology for acute organ injury research.

The position prepares fellows for tenure-track or industrial AI research careers, with access to national data networks and high-performance computing; it runs to 31 August 2027 and is renewable subject to funding and progress. Salary from $63,480 depending on NIH level.

PhD in biomedical informatics, computer science, data science, electrical and computer engineering, applied mathematics or a related quantitative field earned within the past three years, with a strong background in one or more of: generative and trajectory modelling (transformers, mixture-of-experts, reinforcement learning, counterfactual analysis); multimodal NLP and large language models; ontology engineering and knowledge graphs; regulatory science, explainable AI and uncertainty quantification; high-performance computing. Application: resume/CV, letter of interest and three work references (at least one from a supervisor).