stanford bio

The Laboratory of Quantitative Imaging Informatics at Stanford University is recruiting a Postdoctoral Fellow. The Laboratory, led by Dr. Daniel Rubin in the Departments of Radiology and Medicine (Biomedical Informatics Research), focuses on cuttingedge research at the intersection of imaging science and biomedical informatics, integrating and analyzing imaging data with clinical and molecular information to discover the molecular basis for disease, and to use this knowledge to create translational applications that will transform medical care.


The lab is developing methods and tools to discover and to extract quantitative and semantic imaging features of disease to define imaging phenotypes that can predict underlying tissue biological changes and define disease subtypes and to develop optimum treatments. The vision is to bring emerging Big Data approaches to images, and to leverage our vast image archives for data-driven discovery, rapid learning, and decision support. They develop methods in imaging informatics and bioinformatics, including image processing, machine learning, knowledge representation, computer reasoning, natural language processing, and decision theory. Their exciting work is bridging the full spectrum of biomedical imaging, including radiology, pathology, ophthalmology, oncology, and cellular imaging, and it is being done with multidisciplinary collaborations with top scientists at Stanford as well as with other institutions internationally.


Research areas in the laboratory in include: (1) image processing methods to extract novel quantitative imaging features which characterize disease and predict the effectiveness of treatments; (2) automated image segmentation and quantification of abnormalities, correlating them with clinical and molecular features of disease; (3) software tools to annotate, process, and analyze radiology images to enable building a large structured image database for data mining and discovery; (4) methods to retrieve similar images (content based image retrieval) from largescale image archives; (5) Big Data methods for image analysis, federated cloud-based computing with massive image datasets, bringing analysis algorithms to the data and aggregating partial results; (6) rapid learning systems for healthcare through finding patients with similar imaging/clinical/genomic characteristics;  (7) natural language processing of radiology reports to extract semantic features describing images and to enable computerized assessment of image meaning; (8) analyzing integrated imaging and molecular data to discover imaging signatures of disease and its response to treatment; and (9) translation of the prior endeavors into practice by developing "intelligent" image viewing workstations.


Candidates should have postgraduate degree (MD or PhD) and training in Biomedical Informatics, Computer Science, or a related discipline with a background or strong interest in imaging science and a record of distinguished scholarly achievement.


Interested applicants should submit a Curriculum Vitae, a brief statement of research interests, and three letters of reference in one PDF document to rubinlab‐

Lab Web page: