Faculty Positions Focused Artificial Intelligence Approaches to Radiological Imaging

Work type: Non-Tenure Line (Research), University Tenure Line
Location: Stanford University
Categories: School of Medicine

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 to join the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) Division. IBIIS faculty focus on pioneering, translating, teaching and disseminating methods in data science that integrate imaging, clinical, and
-omics data to better understand the molecular mechanisms of diseases and to improve the accuracy and efficiency of clinical care. Recognizing that Artificial Intelligence (AI) techniques, which are likely to be a major focus of this effort, require large sets of integrated imaging, clinical, and outcomes data, the Stanford University School of Medicine has developed and made available a research data warehouse, which includes clinical images and linked data from the electronic medical record, to enable discovery of new relationships between imaging findings and clinical, histological, and genomic manifestations of health and disease.

Appointment will be in the University Tenure Line or Non-Tenure Line (Research). The predominant criterion for appointment in the University Tenure Line is a major commitment to research and teaching. The major criterion for appointment for faculty in the Non-Tenure Line (Research) is evidence of high-level performance as a researcher for whose special knowledge a programmatic need exists. Faculty rank and line will be determined by the qualifications and experience of the successful candidate.

The new faculty will lead an innovative research program developing, translating, benchmarking, and validating AI and other data science methods to improve the practice of radiology by characterizing, reconstructing, enhancing, segmenting, classifying and/or interpreting medical images, and linking them to other imaging (e.g., pathology, ophthalmology,
-omics), clinical, biological and
-omics data. Such integration of imaging with other clinical data could one day enable real-time decision support for early detection of disease, more accurate diagnosis, tailored planning of treatment, precise outcome prediction and other capabilities.

The qualified candidate will have a PhD in computer science, engineering, physics, biomedical informatics, data science, imaging science, or another related field, or an MD. We are particularly interested in candidates who have demonstrated expertise in AI, machine learning and other data science approaches that improve the practice of radiology for some or all of the following: (a) detection of disease and classification of patient data in near real-time, (b) reconstruction, de-noising, and/or otherwise enhancing images, (c) analyzing massive data sets containing both images and non-imaging data sources, (d) creating systems that employ image data to assist human decision makers, (e) developing data resources and tools that catalyze discovery, and (f) rigorously validating AI models pre- and post-deployment to ensure high performance and health equity.

The ideal candidate will have completed a postdoctoral fellowship and have (1) significant research experience resulting in high impact publications and success with grant funding (e.g., an NIH K or R grant) or show potential to achieve, (2) experience in developing and 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 mentoring and training the next generation of researchers.

Please apply online by submitting your curriculum vitae and a candidate statement no longer than 3 pages describing your research, and teaching activities and interests. The Radiology Department, School of Medicine and Stanford University value faculty who will help foster an inclusive academic environment for colleagues, students, and staff with a wide range of…