Postdoctoral Fellow, TEAM-AI Lab, Department of Quantitative and Systems Health Sciences
Core
Lead methodological innovation, software architecture engineering, and scientific execution for active grant portfolios at the intersection of health data science, multimodal AI, digital twins, and computational phenotyping.
Role type
Postdoctoral Research Associate (Health Data Science & AI)
Builds
Production ML models, digital twin frameworks, and clinical intelligence tools for biomedical research.
Domain
Biomedical Informatics / Health Data Science / AI
Deliverable
production ML models
Required skills
Generative and trajectory modeling, transformers, mixture-of-experts, reinforcement learning, simulation, counterfactual analysis, multimodal NLP, large language models (LLMs), cross-attention fusion, vision-language transformers, ontological engineering, knowledge graph construction, high-performance computing, big data analytics
Preferred skills
Model validation, clinical trial design, causal inference, retrieval-augmented generation (RAG), foundation models, real-world clinical dataset analysis, responsible AI, biomedical image processing, scientific programming on Linux
Technologies
LLMs, transformers, reinforcement learning, knowledge graphs, Linux, high-performance computing clusters
Responsibilities
Design and implement mixture-of-experts neural architectures and reinforcement learning pipelines for disease trajectory simulation; Architect and evaluate multi-site cardiotoxicity risk prediction models integrating EHRs and clinical notes; Coordinate AI work streams within national research networks to extract and validate Common Data Elements; Construct deep language models and NLP pipelines to extract oncologic phenotypes; Engineer semantic knowledge graphs and database query architectures for organ injury research; Author high-impact manuscripts in informatics and machine learning journals.
Seniority
Postdoctoral Fellow (Early-career researcher)
