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Assistant Specialist

Recruitment Period

Open date: December 11th, 2018

Last review date: Tuesday, Feb 26, 2019 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.

Final date: Thursday, Jun 11, 2020 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.


Assistant Specialist

An Assistant Specialist position in the area of image computation and/or machine learning applied to breast cancer is available within the UCSF Department of Radiology and Biomedical Imaging. The successful candidate will work in a multidisciplinary research team to develop automated image characterization and feature extraction tools and implement statistical modeling for prediction of outcomes in breast cancer treatment trials. The candidate will play a key role in developing machine learning and radiomics approaches for correlative imaging and molecular biomarker studies of breast cancer.

The position requires a M.S or higher degree with training in quantitative imaging analysis, and bioinformatics and/or machine learning. The ideal candidate will have expertise in statistical analysis and computational skills to analyze complex, high-dimensional datasets with the interest in advanced image analysis and machine learning. Experience in R, Python, C/C++, MatLab, and Unix programming environments is essential. This position requires a highly motivated individual with excellent verbal and written communication skills. Experience in breast MR imaging or breast cancer research would be highly desired. Candidates must possess the required qualifications by the time of hire.

UC San Francisco seeks candidates whose experience, teaching, research, or community service that has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.

Please apply online at

Job location

San Francisco, CA


Document requirements
  • Curriculum Vitae - Your most recently updated C.V.

  • Cover Letter (Optional)

  • Statement of Research (Optional)

  • Statement of Teaching (Optional)

  • Statement of Contributions to Diversity (Optional)

  • Misc / Additional (Optional)