Postdoctoral Fellow - Radiation Physics - Research

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 Brachy Go, a reinforcement learning-based treatment planning framework for cervical cancer brachytherapy.

Brachy Go 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.

• 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.

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.

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.

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