Physics Postdoctoral Fellowship and Certificate Program
The Physics Postdoctoral Fellowship program began with the arrival of Dr. X. Allen Li to the Department of Radiation Oncology in 2004. Dr. Li saw the need for the training of postdoctoral fellows in the area of medical physics and recruited the first physics postdoctoral fellow to train in the Department of Radiation Oncology. Past Postdoctoral fellows performed in-depth research as well as received comprehensive clinical training. The Postdoctoral Fellowship program has had nine fellows graduate from the program.
2009 began the evolution of two separate medical physics training programs, the continuation of the Postdoctoral Fellowship Program and the creation of the new Physics Residency Program. While the Physics Residency Program is a three-year program with a focus on clinical training along with two research semesters, the Physics Postdoctoral Program is for PhD candidates who would like to focus primarily on research with limited and optional clinical physics classes and training.
Realizing the need for a formalized certificate training program, the Department of Radiation Oncology applied for CAMPEP accreditation of the curriculum offered to our postdoctoral fellows. The Physics Certificate Program was CAMPEP accredited in 2013. At this time, the Medical Physics Certificate Program is an internal program only offered to current radiation oncology postdoctoral fellows.
Postdoctoral Fellowship positions vary dependent on available research and research funding. Postdoctoral fellows completing the Physics Certificate Program are able to apply to any CAMPEP accredited Physics Residency programs.
Anyone interested in applying for a postdoctoral fellowship can send a copy of their CV to Jessica Kotowicz. All CVs are reviewed by Dr. X. Allen Li. Qualified applicants are then contacted for further discussion.
Medical Physics Certificate Program Statistics
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*Program accredited by CAMPEP December 2013
Meet our Physics Postdoctoral Fellows
Nguyen Phuong Dang, PhD
Research focused on auto-segmentation using deep learning models.
Jie Ding, PhD
Research focused on developing an auto-refinement process for auto-segmented contours of abdominal organs to accelerate segmentation for MR-guided online adaptive replanning
Juan Garcia Alvarez
Research focused on statistical methods for uncertainty estimation of deformable image registration-based dose accumulation, implementation in automated clinical workflows
Research focused on using the extracellular volume fraction (ECV), estimated from dual energy CT (DECT), to assess treatment response of pancreatic tumors to chemoradiation therapy
Saleh Hamdan, PhD
Research is focused on applying Deep Learning to generate radiation therapy plans accurately and efficiently for Real Time Adaptive Radiation Therapy using the MR-Linac
Development of novel techniques fully account for interfractional anatomical changes in MR-Linac image-guided adaptive radiotherapy for the effective tumor targeting during radiation therapy delivery. Quick assessment of MR‐guided online adaptive radiation therapy workflow on MR‐Linac: Adapt to Position-ATP or Adapt to Shape-ATS
Edwin Quashie, PhD
Research focused on developing a practical method for treatment planning of re-irradiation based on organ-specific biologically effective dose (BED), involving dose accumulation based on deformable image registration (DIR) and calculations of IsoBED curves for different organs at risk (OAR).
Christina Sarosiek, PhD
Research focused on utilizing deep learning methods to automatically correct suboptimal auto-segmented contours on abdominal MR images to accelerate segmentation during MR-guided online adaptive radiotherapy.