Postdoctoral scholar in medical image analysis

Stanford University
Location: 
Stanford, CA
Job Type: 
Full Time
Closing Date: 
Wednesday, May 9, 2018

We are looking for a highly motivated postdoctoral scholar at Stanford University School of Medicine. A major focus of the lab is to develop and validate image-based biomarkers to improve prognostication and prediction of therapy response in cancer. We are also exploring the biological basis of clinically relevant imaging phenotypes by correlating with molecular data. We have been conducting systematic imaging biomarker studies on breast, lung, brain, head and neck cancers, using a variety of modalities including PET, CT, and MRI (Cui et al. Radiology, 2016, Wu et al. Radiology, 2016, Wu et al. Radiology, 2017, Wu et al. Clinical Cancer Research, 2017, Wu et al. Radiology, 2018). For more information about our research, see http://med.stanford.edu/lilab

Candidates with a Ph.D. in biomedical engineering, electrical engineering, computer science, physics, or a related area are invited to apply. Research experience in medical image analysis as evidenced by first-author publications is required. Familiarity with statistical, machine learning (especially deep learning) techniques is highly desired. Strong programming skills (MATLAB, R, Python) are required. We are seeking a highly motivated individual with well-developed communication skills and the desire and talent to tackle challenging technical problems in medicine. This position is fully supported by an NIH grant.

Our lab at Stanford University is dedicated to mentoring and cultivating the future leaders in biomedical research. Postdocs and students in the lab have gone to independent academic career or research labs in industry. Major awards to our trainees include the NIH K99/R00 Pathway to Independence Award, ASTRO Resident Clinical/Basic Science Research Award, ASTRO Basic/Translational Science Award, RSNA Introduction to Academic Radiology for Scientists, presentations at ASTRO Best of Physics and AAPM Science Council Research sessions.

Interested applicants should send a research statement, CV, and names of three references to:

Ruijiang Li, PhD

Stanford University

Email: rli2@stanford.edu