University of Texas at Austin
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3 days ago
Postdoctoral Fellow in AI Healthcare Innovation, Biomedical Informatics, and Multimodal AI The University of Texas at Austin in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Temporary postdoctoral position expected to continue until 2027-08-31, renewable based on funding, work performance, and progress toward goals. Salary starts at $63,480+ depending on NIH level.
Country
United States
University
University of Texas at Austin

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About this position
The University of Texas at Austin is recruiting a Postdoctoral Fellow in AI Healthcare Innovation within the Quantitative and Systems Health Sciences unit and the TEAM-AI Lab at Dell Medical School.
This postdoctoral role focuses on health data science, multimodal AI, digital twins, computational phenotyping, responsible AI, clinical NLP, knowledge graphs, and translational biomedical informatics. The fellow will work under the mentorship of Dr. Hongfang Liu and lab faculty on multiple active grant projects, including EMED, CardioOnco-AI, ReCARDO, WONDER, and POI-KB.
Core responsibilities include designing mixture-of-experts neural architectures, reinforcement learning pipelines, multimodal risk prediction models, clinical NLP systems, ontological engineering, and database/query architectures for real-world health data. The position also emphasizes manuscript writing and collaboration across multi-institutional research networks.
Eligibility highlights: applicants must hold a PhD in Biomedical Informatics, Computer Science, Data Science, Electrical & Computer Engineering, Applied Mathematics, or a related quantitative field, and the PhD must have been earned within the last three years. Preferred experience includes transformer models, LLMs, RAG, causal inference, clinical prediction modeling, responsible AI, fairness/privacy/explainability, biomedical image processing, radiomics, digital pathology, and HPC/Linux scientific programming.
Funding and terms: this is a full-time temporary postdoctoral appointment expected to continue until 2027-08-31, renewable depending on funding, performance, and progress. Salary starts at $63,480+ depending on NIH level.
How to apply: submit the application through the UT Austin Workday posting. Prepare a CV/resume, three references with contact information, and a letter of interest. Current UT employees and contingent workers must apply through Workday/Find UT Jobs and upload materials in the application questions.
Funding details
Temporary postdoctoral position expected to continue until 2027-08-31, renewable based on funding, work performance, and progress toward goals. Salary starts at $63,480+ depending on NIH level.
What's required
PhD 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 generative and trajectory modeling, including transformers, mixture-of-experts, reinforcement learning, simulation, and counterfactual analysis; multimodal NLP and fusion, large language models, cross-attention fusion, and vision-language transformers; ontological engineering, knowledge graph construction and mining, and common data element development for data harmonization; regulatory science and explainable AI, verification/validation, uncertainty quantification, and AI evaluation frameworks; and high-performance computing and big data analytics. Preferred experience includes model validation, clinical trial design, causal inference, transformer-based models, retrieval-augmented generation, foundation models, complex real-world clinical datasets, responsible AI, fairness, privacy, explainable AI, biomedical image processing, radiomics, digital pathology, and Linux/HPC scientific programming.
How to apply
Apply through the Workday job posting. Submit a resume/CV, three work references with contact information, and a letter of interest. Current UT employees must apply through Workday/Find UT Jobs and upload required materials in the application questions.
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