Postdoctoral Fellow - Radiation Physics - Research
- Requisition #: 600662-202609091034
- Department: Radiation Physics - Research
- Location: Houston, TX
- Posted Date: 9/3/2026
The Department of Radiation Physics at The University of Texas MD Anderson Cancer Center is seeking a highly motivated postdoctoral fellow to work in in Dr. Xiao Liang's lab and develop BrachyGo, an AI-based treatment planning framework for cervical cancer brachytherapy. BrachyGo will contribute to the broader Brachytherapy Planning Assistant (BPA), a web-based platform designed to streamline high-quality brachytherapy planning and expand access to advanced treatment planning methods.
The fellow will develop artificial intelligence and deep learning methods for patient-specific recurrence-risk prediction. The research will include development of spatially resolved three-dimensional recurrence-risk maps from multimodal clinical and imaging data to support automated, patient-specific treatment planning. The fellow will work closely with medical physicists, radiation oncologists, AI researchers, and collaborators in automated radiation treatment planning, with 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
By the completion of the training, the fellow is expected to:
1. Develop advanced expertise in deep learning and artificial intelligence methods for medical imaging and radiation oncology.
2. Develop and evaluate multimodal AI models for patient-specific recurrence-risk prediction.
3. Gain experience integrating patient anatomy, applicator geometry, dose distributions, recurrence-risk information, and clinical planning objectives into treatment-planning algorithms.
4. Develop and evaluate automated brachytherapy treatment plans and compare AI-generated plans with expert clinical plans.
5. Gain experience translating AI methods into clinically meaningful radiation treatment-planning tools.
6. Develop skills in scientific communication, including manuscript preparation, conference presentations, and multidisciplinary collaboration.
ELIGIBILITY REQUIREMENTS
1. Ph.D. or equivalent doctoral degree in medical physics, physics, biomedical engineering, computer science, electrical engineering, or a related quantitative field.
2. Candidates with prior experience in machine learning, deep learning, reinforcement learning, or medical image analysis are strongly preferred.
3. Experience with Python and machine-learning frameworks such as PyTorch is desirable.
4. Background in medical physics, radiation therapy treatment planning, optimization, medical imaging, or DICOM is advantageous but not required.
5. Applicants must meet MD Anderson requirements for postdoctoral appointment, including recent completion of doctoral education or qualifying prior postdoctoral experience/graduate medical education.
ADDITIONAL APPLICATION INFORMATION
Applicants should submit:
CV
Brief Statement describing research interests and relevant experience
Contact information for references
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
The fellow will develop artificial intelligence and deep learning methods for patient-specific recurrence-risk prediction. The research will include development of spatially resolved three-dimensional recurrence-risk maps from multimodal clinical and imaging data to support automated, patient-specific treatment planning. The fellow will work closely with medical physicists, radiation oncologists, AI researchers, and collaborators in automated radiation treatment planning, with 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
By the completion of the training, the fellow is expected to:
1. Develop advanced expertise in deep learning and artificial intelligence methods for medical imaging and radiation oncology.
2. Develop and evaluate multimodal AI models for patient-specific recurrence-risk prediction.
3. Gain experience integrating patient anatomy, applicator geometry, dose distributions, recurrence-risk information, and clinical planning objectives into treatment-planning algorithms.
4. Develop and evaluate automated brachytherapy treatment plans and compare AI-generated plans with expert clinical plans.
5. Gain experience translating AI methods into clinically meaningful radiation treatment-planning tools.
6. Develop skills in scientific communication, including manuscript preparation, conference presentations, and multidisciplinary collaboration.
ELIGIBILITY REQUIREMENTS
1. Ph.D. or equivalent doctoral degree in medical physics, physics, biomedical engineering, computer science, electrical engineering, or a related quantitative field.
2. Candidates with prior experience in machine learning, deep learning, reinforcement learning, or medical image analysis are strongly preferred.
3. Experience with Python and machine-learning frameworks such as PyTorch is desirable.
4. Background in medical physics, radiation therapy treatment planning, optimization, medical imaging, or DICOM is advantageous but not required.
5. Applicants must meet MD Anderson requirements for postdoctoral appointment, including recent completion of doctoral education or qualifying prior postdoctoral experience/graduate medical education.
ADDITIONAL APPLICATION INFORMATION
Applicants should submit:
CV
Brief Statement describing research interests and relevant experience
Contact information for references
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
