Stanford University — Faculty Position in Integrative Biomedical Imaging Informatics

The Department of Radiology at Stanford School of Medicine is recruiting a full-time faculty member at the Assistant, Associate, or Full Professor rank in the Research Line or University Tenure Line to join the Division of Integrative Biomedical Imaging Informatics at Stanford (IBIIS). 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. Faculty rank will be determined by the qualifications and experience of the successful candidate. The Department of Radiology at Stanford University is expanding, with significant growth in patient care facilities, foundational research, translational science, and clinical care. Exceptional opportunities are available in all aspects of imaging informatics research.

The candidate will lead a broad research program developing and validating methods and tools to characterize medical images, and to combine the information they contain with clinical, biological, and genomic data to diagnose disease and manage the health of individuals and populations. 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. Medical images and other information from patients’ medical records could be aggregated and analyzed continuously, thereby enabling continuous discovery of new relationships between imaging findings and clinical, histological, and genomic manifestations of disease.

The qualified candidate will have a PhD with a background in imaging science, computer science, engineering, physics, biomedical informatics, data science, or other related field. We are particularly interested in candidates who have demonstrated, through publications, extramural funding and awards, expertise in broadly applicable machine learning and other algorithms and methods that enable (a) construction of large-scale searchable databases integrating images, radiology reports, and other aspects of the medical record, including clinical outcome, response to therapy, results of other diagnostic tests, and molecular and multi-omic analysis of tissue leading to the development and testing of patient-specific and population-scale algorithms, possibly including wet-lab investigations, (b) image analysis, federated with other databases if needed, to identify critical findings from imaging examinations in near real-time, (c) natural language processing to extract discrete data from human interpretations of images, (d) analysis of massive data sets containing both images and data collected from patient monitors, wearable devices, medical records, patient self-reports, and disease-specific early detection tests, and (e) creation of decision support systems that integrate image data with all other patient level data.

The candidate must have demonstrated interest in translating these algorithms and/or methods into clinical settings, and the desire to seek translational collaborations with a broad range of investigators, including faculty in the Schools of Medicine, Engineering, and Humanities & Sciences, and investigators pursuing similar research goals outside of Stanford. The ideal candidate will have demonstrated significant research experience resulting in high impact publications, and success with grant funding (e.g., an NIH K* or R* grant). We seek motivated individuals who are committed not only to excellence in research, but also to training the next generation of researchers in integrative biomedical imaging informatics.

Stanford University is an equal opportunity employer and is committed to increasing the diversity of its faculty. It welcomes nominations of and applications from women, members of minority groups, protected veterans and individuals with disabilities, as well as from others 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 future goals on our department website: