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Postdoctoral Fellowship

Current projects seeking postdoctoral fellows

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

The 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. 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 their 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 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. or equivalent are not permitted to engage in patient care activity.

Submit an application

To apply for consideration, please submit your full CV (to include education, research appointments and all publications), research statement/cover letter and recommendation letters on the MD Anderson Job Application System or contact Dr. Chengyue Wu.