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

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

Dell Medical School is seeking a Postdoctoral Fellow, TEAM-AI Lab for the Department of Quantitative and Systems Health Sciences. 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.

PhD must have been received within the last three years.

The candidate will hold primary responsibility for designing novel algorithmic frameworks, coordinating multi-institutional research networks, and translating real-world health data into actionable clinical intelligence. This position provides structured preparation for an academic tenure-track career or lead research scientist position in industrial AI labs, providing access to national data networks, high-performance computing clusters, and clinical interdisciplinary collaborations across UT Austin.

This position is a temporary with an end date of 08/31/27, renewable based upon availability of funding, work performance, and progress toward goals.

Responsibilities include:

• Leading 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.
• Architecting and evaluating 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 Cardio Onco-AI award.
• Coordinating 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.
• Constructing 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.
• Engineering 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.

Minimum qualifications include:

• 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 framework
• High-performance computing and big data analytics
• PhD must have been received within the last three years.

Preferred qualifications include:

• 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.

Salary range is $63,480+ depending on NIH Level.

Working environment/equipment includes:

• standard office equipment.
• repetitive use of a keyboard.
• may be exposed to communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications and disoriented or combative patients, or others.
• 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.
• may require…