Postdoctoral Scholar in Health AI-Pediatrics CBMI

UT Health Sciences; Memphis (TN); On-site; USD 60,000 - 80,000; Full time

Job summary

UT Health Sciences in Memphis, TN seeks a Postdoctoral Scholar to design and optimize advanced AI methods for intelligent cancer patient navigation and clinical decision support. You will lead research in multimodal machine learning, explainable AI, causal inference, and predictive analytics, collaborating with clinicians and engineers to translate findings into interoperable tools.

The role requires a PhD in a relevant field, a strong publication record, and excellent coding skills.

Qualifications

• Ph.D. in a relevant discipline with strong publication record.
• Track record of publications in top journals and conferences.
• Strong coding and implementation skills in AI/ML.
• Excellent written and verbal communication abilities.

Responsibilities

• Designs, develops, implements, and optimizes AI and ML models for cancer navigation and decision support.
• Conducts research in explainable/agentic AI, causal inference, and predictive analytics.
• Assesses algorithms using diverse healthcare data like EHRs and imaging.
• Performs benchmarking, validation, and fairness/robustness analyses.
• Collaborates with engineers and clinicians to translate research into clinical tools.
• Maintains reproducible workflows and research software.
• Mentors graduate students and junior researchers.
• Prepares manuscripts and conference presentations; supports grant-funded work.
• Contributes to proposals and collaborative research activities.

Skills

Advanced AI concepts

Multimodal data understanding

Collaboration

Mentoring

Education

PhD in Medical Informatics / CS / Math

Tools

Python

TensorFlow

PyTorch

EHR data analysis

Job description

Job Description

THIS IS A GRANT-FUNDED POSITION FUNDED UNTIL OCTOBER 1, 2027.

The Postdoctoral Scholar designs, develops, evaluates, and optimizes advanced artificial intelligence (AI) methods that support intelligent cancer patient navigation and clinical decision support. Under the direction of the Principal Investigator, this position leads research and development on multimodal machine learning, agentic AI, explainable AI, causal inference, predictive analytics, and continuous learning to develop trustworthy AI solutions that improve cancer care delivery.

Responsibilities

• 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.