Medical Image Computing and Analysis (MICA) Lab in the Division of Medical Physics, Department of Radiation Oncology and Molecular Radiation Sciences, and also affiliated with Carnegie Center for Surgical Innovation at Johns Hopkins University has multiple postdoctoral research fellow positions opening for a highly qualified personnel to work on research in medical image processing and analysis using artificial intelligence (AI) technology.

Multidisciplinary team of physicists, computer scientists, engineers, and physicians are working on image-guided surgery and radiation therapy problems in which the fellow will work on AI-based organ segmentation, multi-modal image registration, real-time image guidance, and imaging biomarker for treatment response prediction/assessment. Areas of major clinical application include image-guided radiation therapy of cancers in cervix, prostate, spine, and head and neck in close collaboration with radiation oncologists.

An ideal candidate should have a Ph.D. in biomedical or electrical engineering, computer science, or medical physics with a strong background and interest in medical image processing/analysis and machine learning. Programming experience in python, Matlab, C++, ITK/VTK, CUDA, tensorflow, pytorch is a plus.

This is an exciting research opportunity to learn and work on latest and cutting edge techniques in medical image processing, machine learning, multi-modality imaging, and image-guided interventions in collaboration with multi-disciplinary team. Interested applicants should send their CV and names of three professional references to:

Junghoon Lee, Ph.D.
Associate Professor
Division of Medical Physics
Department of Radiation Oncology and Molecular Radiation Sciences
Johns Hopkins University School of Medicine
Email: junghoon (at)

The Johns Hopkins University is an EEO/AA Employer. The Department of Radiation Oncology and Molecular Radiation Sciences is committed to building a diverse educational environment, and women and minorities are strongly encouraged to apply.