Postdoctoral Fellow - Radiation Physics - Research

UT MD Anderson; Houston (TX); On-site; USD 64,000 - 76,000; Full time

Job summary

The University of Texas MD Anderson Cancer Center in Houston seeks a highly motivated postdoctoral fellow to develop AI methods for personalized cervical cancer brachytherapy. The role centers on the BrachyGo framework and BPA platform to learn planning strategies and generate patient-specific treatment plans.

You will collaborate with medical physicists, radiation oncologists, and AI researchers to explore reinforcement learning, multimodal deep learning, and recurrence-risk mapping, with

Qualifications

• Ph.D. in a related field within the past three years.
• Experience in ML/DL/RL or medical image analysis is highly preferred.
• Familiarity with medical imaging and DICOM is advantageous but not required.

Responsibilities

• Develop AI methods for brachytherapy planning and recurrence-risk mapping.
• Collaborate with medical physicists, radiation oncologists, and AI researchers.
• Contribute to clinical shadowing, meetings, conferences, and publications.
• Evaluate and validate models under real-world clinical constraints.

Skills

Machine learning

Deep learning

Reinforcement learning

Medical image analysis

Education

Ph.D. or equivalent in medical physics, physics, biomedical engineering, computer science, electrical engineering or related quantitative field

Tools

Python

DICOM

TensorFlow

Job description

The Department of Radiation Physics at The University of Texas MD Anderson Cancer Center is seeking a highly motivated postdoctoral fellow to develop AI methods for personalized cervical cancer brachytherapy. The position will support two complementary, but independent research aims. This position will work in the lab of Dr. Shiqin Su. The first aim will focus on BrachyGo, a reinforcement learning-based treatment planning framework for cervical cancer brachytherapy. BrachyGo is designed to learn expert planning strategies and generate patient-specific treatment plans and will contribute to the broader Brachytherapy Planning Assistant (BPA), a web-based platform intended to streamline high-quality brachytherapy planning and expand access to advanced planning methods.

The second aim will focus on patient-specific recurrence-risk prediction, including development of spatially resolved three-dimensional recurrence-risk maps from multimodal clinical and imaging data. This work will use machine-learning and deep-learning approaches to characterize spatial patterns of treatment failure and support individualized treatment strategies. The fellow will develop and evaluate reinforcement learning, machine-learning, and deep-learning methods across both aims and will work closely with medical physicists, radiation oncologists, AI researchers, and collaborators in automated radiation treatment planning. The position provides opportunities for clinical shadowing, multidisciplinary research meetings, conference presentations, and peer-reviewed publications.

All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.

LEARNING OBJECTIVES

• Develop advanced expertise in either reinforcement learning for automated treatment planning or multimodal deep learning for patient-specific recurrence-risk prediction, depending on the fellow's primary research focus.
• Develop expertise in formulating clinically relevant radiation oncology problems as machine-learning prediction or optimization tasks, including model development, validation, and interpretation using multimodal medical data.
• Gain experience developing and rigorously evaluating AI methods under real-world clinical constraints, including comparison with established clinical approaches and assessment of robustness and generalizability.
• Develop experience translating AI research into reproducible clinical research workflows, working with multidisciplinary collaborators, and leading dissemination through peer-reviewed publications and scientific presentations.

Eligibility Requirements

Ph.D. or equivalent doctoral degree in medical physics, physics, biomedical engineering, computer science, electrical engineering, or a related quantitative field, awarded within the past three years.

Preferred qualifications:

Experience in one or more of the following areas is strongly preferred: machine learning, deep learning, reinforcement learning, or medical image analysis.

Experience in medical physics, radiation therapy treatment planning, optimization, medical imaging, or DICOM is advantageous but not required.

POSITION INFORMATION

MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition

Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.

This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html