Postdoctoral Scholar in Health AI-Pediatrics CBMI
University of Tennessee at Martin
Salary undisclosed
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Description
Designs, develops, implements, and optimizes advanced artificial intelligence, machine learning, and multimodal AI models for intelligent cancer patient navigation and clinical decision support.
Conducts research in explainable AI, agentic AI, causal inference, predictive analytics, knowledge representation, and continuous learning.
Designs and evaluates AI algorithms using electronic health records, patient-reported outcomes, social determinants of health, medical imaging, and other healthcare data sources.
Conducts benchmarking, validation, performance evaluation, and fairness, robustness, and explainability assessments of AI model.
Collaborates with software engineers, clinicians, and interdisciplinary investigators to translate AI research into interoperable clinical applications and decision support tools.
Develops and maintains reproducible analytical workflows and research software to support AI model development and evaluation
Mentors graduate students and junior researchers throughout the project lifecycle.
Prepares manuscripts, technical reports, conference presentations, and publications in leading journals and scientific meetings.
Participates in proposal preparation and collaborative research activities supporting federally and state-funded research programs.
Performs other duties as assigned.
Qualifications
Ph.D. in a relevant discipline (e.g. Medical Informatics, Computer Science, Software Engineering, Mathematics, etc.)
Track record of publications in top journals and conferences in the field. Strong track record of quantitative and analytics mastery, and expertise in Artificial Intelligence, Machine Learning, Causal Modeling, and Knowledge Graphs. Strong coding and implementation skills. Outstanding interpersonal skills and written and verbal communication capabilities.
WORK SCHEDULE: This position may occasionally be required to work weekends and evenings. May require occasional overnight travel.
About this role
At the University of Tennessee at Martin's Center for Biomedical Informatics in Memphis, this postdoctoral role centers on building and refining AI systems designed to improve cancer patient care. You'll work across the full pipeline—from designing machine learning and multimodal AI models through validation and clinical implementation—with a focus on explainability, causal inference, and fairness. The work draws on diverse data sources including electronic health records, imaging, and patient-reported outcomes to create decision support tools that clinicians can actually trust and use.
This position suits someone with a Ph.D. in a quantitative field (medical informatics, computer science, mathematics, or related discipline) who has already published in top-tier venues and has solid hands-on coding ability. You'll need genuine expertise in AI, machine learning, causal modeling, and knowledge representation—not just familiarity. Beyond technical depth, the role demands strong collaboration skills; you'll translate research into working clinical applications alongside software engineers and clinicians, mentor junior researchers, and communicate findings through papers, reports, and presentations.
The work is based in Memphis and requires in-office presence, with occasional evening or weekend hours and travel as needed. You'll contribute to federally and state-funded research programs and help shape how AI gets deployed in real clinical settings for pediatric cancer patients.
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