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Machine Learning in Medical Imaging

National Cancer Institute, Bethesda, MD and surrounding area

Position Description:

Seeking highly motivated research fellows to join the Artificial Intelligence Resource (AIR) Program within the Center for Cancer Research at National Cancer Institute (NCI), National Institutes of Health (NIH). AIR focuses on translational applications of Artificial Intelligence (AI) and computer vision in medical image analysis, including both radiology, digital pathology, and other medical imaging fields. This dynamic group works with clinical researchers across NCI to identify AI projects and solutions for oncology tasks related to disease diagnosis, monitoring, and prognosis. All fellows will be expected to interface with senior AIR staff, clinical investigators, and other research fellows during design, development, and testing of algorithms. Fellows should anticipate being involved in all components of database development pertaining to each application, including dataset curation, annotation, and evaluation.


  • Candidates should hold a PhD degree in computer science, biostatistics/informatics, biomedical or electrical engineering, physics, mathematics or related fields.
  • Desirable skills: should be able to demonstrate programming knowledge/skills in one or more languages including Python, R, MATLAB, C/C++, etc.
  • Publication record of prior experience or expertise in quantitative image analysis or deep learning and/or machine learning is required, ideally in computer vision but not required.
  • A successful candidate will be able to demonstrate ability to design and execute independent research projects including peer-reviewed publications, preparing and presenting preliminary data at research conferences, and strong communication skills to broad audience.
  • 1-2 year appointments renewable thereafter.

To Apply:

Please submit your CV, statement of professional goals and two letters of reference to the following email address:

The NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities.