Postdoc Fellow - Imaging Physics

About the role

A postdoctoral fellowship position is available in the Department of Imaging Physics in the laboratory of Chengyue Wu, PhD. Dr. Chengyue Wu's research focuses on computational precision oncology, integrating computational/mathematical approaches with emerging biomedical imaging techniques to improve cancer diagnosis, prognosis, and treatment.

Responsibilities

• Engage in highly productive interdisciplinary research projects in image-guided precision oncology and personalized cancer healthcare.
• Expand knowledge and skills in quantitative imaging, image analysis, artificial intelligence (AI)/deep learning technologies, mathematical biomechanical modeling, inverse problems, and uncertainty quantification.
• Contribute to ongoing research projects and explore new areas of research interest under mentorship.
• Work closely with research/clinical collaborators, communicate findings through reports, abstracts, presentations, and publications.
• Participate in seminars, conferences, and related academic endeavors.

Qualifications

• Earned a Ph.D. in natural sciences, computer sciences, applied mathematics, engineering, or related fields, or a medical degree.
• Experience with machine learning and deep learning techniques, mathematical modeling, or medical image analysis is preferred.
• Not required to be US citizens or permanent residents.
• This appointment is not part of a clinical training program; individuals holding an M.D. degree or equivalent are not permitted to engage in patient care activity.

Learning Objectives

The postdoctoral fellow will engage in highly productive interdisciplinary research projects in image-guided precision oncology and personalized cancer healthcare. They will expand their knowledge and skills in quantitative imaging, image analysis, artificial intelligence (AI)/deep learning technologies, mathematical biomechanical modeling, inverse problems, and uncertainty quantification. They will have opportunities to contribute to ongoing research projects and will be encouraged to explore and develop new areas of research interest with guidance from the mentor. They will be expected to work closely with research/clinical collaborators, communicate findings via reports, abstracts, presentations, and publications, and actively participate in seminars, conferences, and related academic endeavors.

Eligibility Requirements

• Earned a Ph.D. in natural sciences, computer sciences, applied mathematics, engineering, or related fields, or a medical degree.
• Experience with machine learning and deep learning techniques, mathematical modeling, or medical image analysis is preferred.
• Not required to be US citizens or permanent residents.
• This appointment is not part of a clinical training program; individuals holding an M.D. degree or equivalent are not permitted to engage in patient care activity.

Additional Application Information

• The trainee will be appointed for one year from the date of hire with an option to be renewed for up to three years.

Position Information

• 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.