Stanford’s AIMI Center is hiring talented individuals who bring new perspectives and skills to our multi-disciplinary team. If you are looking for collaborative clinicians and engineers across multiple Stanford schools, and exciting projects with high clinical relevance, then you are in the right place.
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.
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 90 faculty from the Schools of Medicine and Engineering to develop and evaluate machine learning methods for medical images to improve the health of patients.
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.
Please click here to submit your curriculum vitae and a statement describing your clinical, teaching, and research activities and interests.
Those seeking post-doctoral scholar positions in the AIMI Center who have at least one first-author publication in machine learning for medical imaging and excellent verbal communication skills are encouraged to contact us at firstname.lastname@example.org for current opportunities.
Postdoctoral Research Fellowship at Stanford Radiology
The laboratories of Dr. Heike Daldrup-Link and Dr. Daniel Rubin at Stanford University are recruiting a postdoctoral research fellow to develop technically innovative and clinically impactful deep learning applications to improve the assessment of childhood cancers with positron emission tomography (PET) and magnetic resonance imaging (MRI). The goal is to shorten scan-time, while still getting scans of diagnostic quality, and to generate automated treatment response assessments. Required technical capabilities: Knowledge and hands-on experience with Deep Learning techniques, particularly encoder-decoder networks and super-resolution techniques and methods for image segmentation. Preferably an interest and expertise in AI for medical imaging. Stanford University is an Equal Opportunity/Affirmative Action Employer, and applications from women and minority candidates are strongly encouraged. Interested candidates can send their CV and three letters of recommendation to Dr. Daldrup-Link.