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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

Year Applicants in year
Accepted in year
Graduated in year
Accepted to residency in year
Employed in clinical setting in year
 Employed in research setting in year
 *2013  3  3  NA      
 2014  2  2  1  1    
 2015  4  4
 2  2    
 2016  1  1  2  1  1  
 2017  4  4  4  3    1
 2018  5  5  1  1    
 2019  1  1  4  4    
 2020   5  5  3  2    
 2021  3  3  2  2  1  
 2022  2   2   3   2    

*Program accredited by CAMPEP December 2013

Working at the Medical College of Wisconsin

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Meet our Physics Postdoctoral Fellows


Silambarasan Anbumani, PhD

Postdoctoral Fellow


Research focused on prediction and assessment of radiation treatment response using quantitative imaging, CT biomarkers, and radiomics.


Renae Conlin, PhD

Postdoctoral Fellow


Research focuses on deep learning auto-segmentation models that concentrate on the auto-segmentation of abdominal anatomy based on MR images acquired during MR-guided adaptive radiation therapy.


Samira Dabaghmanesh, PhD

Postdoctoral Fellow


Research focused on extracting organ-specific radiobiological model parameters for selected abdominal and pelvic organs and to demonstrate their applications in determining OAR dose constraints.


Nguyen Phuong Dang, PhD

Postdoctoral Fellow


Research focused on auto-segmentation using deep learning models.


Juan Garcia Alvarez, PhD

Postdoctoral Fellow


Research focused on statistical methods for uncertainty estimation of deformable image registration-based dose accumulation, implementation in automated clinical workflows.


Saleh Hamdan, PhD

Postdoctoral Fellow


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.


Christina Sarosiek, PhD

Postdoctoral Fellow


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.


Mohammad Zarenia, PhD

Postdoctoral Fellow


Research focused on developing auto-correction models for auto-segmented contours of abdominal organs to accelerate segmentation during MR-guided online adaptive radiotherapy.