The Department of Radiology at Stanford School of Medicine is recruiting a full-time faculty member at the level of Assistant, Associate, or Full Professor in the University Tenure Line or the Research Line to join the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) Section and the newly-established Center for Artificial Intelligence in Medicine and Imaging (AIMI Center). IBIIS faculty focus on pioneering, translating, and disseminating methods in the information sciences that integrate imaging, clinical, and molecular data to understand biology and to improve clinical care. The AIMI Center includes more than 40 faculty from the Schools of Medicine and Engineering to develop and evaluate machine learning methods for medical images to improve the health of patients. To support these research programs, Stanford University School of Medicine has developed a research data warehouse where clinical images and other patient information are aggregated and linked, thereby enabling discovery of new relationships between imaging findings and clinical, histological, and genomic manifestations of disease.
The predominant criterion for appointment in the University Tenure Line is a major commitment to research and teaching. The major criterion for appointment in the Research Line is evidence of outstanding performance as a researcher with special knowledge in an area for which a programmatic need exists. The Department of Radiology at Stanford University is expanding, with significant growth in patient care facilities, foundational research, and translational science. Exceptional opportunities are available in all aspects of imaging informatics research. The faculty rank and line will be determined by the qualifications and experience of the successful candidate.
The candidate will lead a broad research program developing and validating machine learning methods and other tools to characterize, reconstruct, enhance, segment, or classify medical images. Often these methods require not only imaging information but also clinical, biological, or genomic data. The integration of imaging information with other data sources could one day enable real-time decision support for early detection of disease and more accurate diagnosis, tailored planning of treatment, and precise prediction of outcome.
The qualified candidate will have a PhD with a background in computer science, engineering, physics, biomedical informatics, data science, imaging science, or other related field. We are particularly interested in candidates who have demonstrated expertise in broadly applicable machine learning and other algorithms and methods that enable image analysis, federated with other databases if needed, to (a) detect and classify objects in near real-time, (b) reconstruct, de-noise, or otherwise enhance images, (c) analyze massive data sets containing both images and other data sources, and (d) create systems that employ image data to assist human decision makers.
The ideal candidate will have (1) significant research experience resulting in high impact publications and success with grant funding (e.g., an NIH K or R grant), (2) experience in translating algorithms and/or methods into practical settings, and (3) the desire to seek translational collaborations with a broad range of investigators pursuing similar research goals inside and outside of Stanford.
We seek motivated individuals who are committed not only to excellence in research, but also to training the next generation of researchers.
Stanford is an equal employment opportunity and 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, protected veteran status, or any other characteristic protected by law. Stanford welcomes applications from all who would bring additional dimensions to the University’s research, teaching and clinical missions.
Interested candidates should submit their CV and a statement of research interests, accomplishments, and goals on our department website: http://radjobs.stanford.edu.