Postdoc: AI for Cancer Care & Clinical Decision Support

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

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.