Postdoctoral Fellow - Radiation Physics - Research
UT MD Anderson, Houston, TX, United States
A postdoctoral fellow position is available in the department of Radiation Physics.
This position focuses on the development and implementation of cutting‑edge AI solutions for radiotherapy.
The successful candidate will join a dynamic research team dedicated to developing an end‑to‑end deep learning framework for autonomous 3D dose calculation and VMAT optimization.
The primary focus of this role is the development of generative models (e.g., Diffusion models, Transformers) to predict optimal machine parameters.
Learning Objectives
The primary objective is to gain advanced expertise in developing complex AI architectures for medical applications.
The fellow will learn to integrate AI‑driven solutions into radiotherapy workflows, specifically for VMAT planning, 3D dose calculation, and machine parameter optimization.
Furthermore, the program will enhance the candidate’s scientific writing skills and provide practical experience in the clinical application of medical physics.
Eligibility Requirements
Applicants must hold a Ph.D. in Medical Physics, Computer Science, Electrical Engineering, or a closely related quantitative field.
A strong proficiency in Python and PyTorch is essential, along with extensive experience in medical image processing and deep learning frameworks.
Additionally, candidates are required to demonstrate a proven track record of academic excellence through peer‑reviewed publications in reputable journals.
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.
EEO Affirmative Action: The University of Texas MD Anderson Cancer Center is an equal opportunity employer in accordance with applicable federal, state, and local laws.
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This position focuses on the development and implementation of cutting‑edge AI solutions for radiotherapy.
The successful candidate will join a dynamic research team dedicated to developing an end‑to‑end deep learning framework for autonomous 3D dose calculation and VMAT optimization.
The primary focus of this role is the development of generative models (e.g., Diffusion models, Transformers) to predict optimal machine parameters.
Learning Objectives
The primary objective is to gain advanced expertise in developing complex AI architectures for medical applications.
The fellow will learn to integrate AI‑driven solutions into radiotherapy workflows, specifically for VMAT planning, 3D dose calculation, and machine parameter optimization.
Furthermore, the program will enhance the candidate’s scientific writing skills and provide practical experience in the clinical application of medical physics.
Eligibility Requirements
Applicants must hold a Ph.D. in Medical Physics, Computer Science, Electrical Engineering, or a closely related quantitative field.
A strong proficiency in Python and PyTorch is essential, along with extensive experience in medical image processing and deep learning frameworks.
Additionally, candidates are required to demonstrate a proven track record of academic excellence through peer‑reviewed publications in reputable journals.
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.
EEO Affirmative Action: The University of Texas MD Anderson Cancer Center is an equal opportunity employer in accordance with applicable federal, state, and local laws.
#J-18808-Ljbffr