Postdoc Fellow - Imaging Physics

UT MD Anderson; Houston (TX); On-site; USD 50,000 - 70,000; Full time

Job summary

A leading cancer research institution is offering a postdoctoral fellowship in Imaging Physics. The role emphasizes interdisciplinary research in computational precision oncology, engaging in projects aimed at optimizing cancer treatment through advanced imaging and modeling techniques. Candidates with a Ph.D. in relevant fields and experience in machine learning are encouraged to apply. This position provides opportunities for professional growth in a collaborative environment.

Qualifications

• Ph.D. in a relevant field is required.
• Experience with machine learning and deep learning techniques is preferred.
• Familiarity with medical image analysis is beneficial.

Responsibilities

• Engage in interdisciplinary research projects in precision oncology.
• Develop and validate image processing methods.
• Communicate findings via reports, presentations, and publications.

Skills

Machine Learning

Deep Learning

Mathematical Modeling

Medical Image Analysis

Quantitative Imaging

Education

Ph.D. in natural sciences, computer sciences, applied mathematics, engineering, or related fields

Job description

A postdoctoral fellowship position is available in the Department of Imaging Physics in the laboratory of Chengyue Wu, PhD. Dr. Chengyue Wu's research interests focus on computational precision oncology, especially integrating computational/mathematical approaches with emerging biomedical imaging techniques to improve the diagnosis, prognosis, and treatment of human cancers. Dr. Wu has extensive experience in developing and validating image processing methods and image-guided models for investigating tumor growth and treatment response, tumor-associated vasculature and microenvironment, and drug delivery. The lab is in a highly collaborative research environment with access to world-class resources, expertise, and data.

Current projects seeking postdoctoral fellows include:

• Image-guided computational modeling ("digital twins") to predict and optimize cancer (especially breast cancer) treatment response on a patient-specific basis.
• Development of deep learning models, longitudinal image analysis, and multi-modality data integration to improve breast cancer early detection.

LEARNING OBJECTIVES

This postdoctoral fellow will engage in highly productive interdisciplinary research projects in image-guided precision oncology and personalized cancer healthcare. The fellow 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. The fellow 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. The fellow 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

Applicants should have earned a Ph.D. in one of the 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. Applicants do not need to be U.S. 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. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

FACULTY MENTOR

Dr. Chengyue Wu