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.
The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and high-quality patient care. It is the only UC campus in the 10-campus system dedicated exclusively to the health sciences.