|Title: Computer Vision / Machine Learning R&D Engineer||Posted: June 19, 2014|
|Location: Redwood City, California USA|
|Department: Research and Development|
Description: An exciting and innovative healthcare seeking to transform cardiology with computer vision and physiological simulation is looking for a talented computer vision engineer whose primary responsibility will be to create, develop and evaluate new algorithms for medical image analysis using a large database of training examples. We expect to be active in the research community, so interest and ability to publish, present and collaborate externally is encouraged.
M.S. or Ph.D. in Computer Science or related field. Ph.D. strongly preferred.
Recent graduates are welcome. Industry work experience is a plus.
Strong knowledge of machine learning and computer vision.
Ability to create theoretically sound and practical solutions to computer vision problems on large, real-world datasets.
Proficient in C/C++ to implement, evaluate and execute algorithms.
Experience with medical or 3D images is preferred, but not essential.
Communication skills to work together with a team and present work internally and externally through presentations and publications.
HeartFlow is a mid-sized company located in Redwood City, CA which started from Stanford (by Prof. Charles Taylor and Christopher Zarins, MD). The company goal is to transform cardiovascular healthcare using precise personalized anatomical models derived from imaging, combined with accurate blood flow simulation to assess how to best treat a patient. Our first product, FFRct, targets coronary artery blockages and has received a lot of press and excitement in the cardiology community, since it will make it possible to avoid an invasive diagnostic catheter procedure that is both expensive, widespread and dangerous (1% mortality during this procedure). The core technology of the company is accurate fluid simulation and robust computer vision.
Our business model gives us access to an unprecedented amount of highly annotated medical images, which makes it possible to leverage computer vision and machine learning to quickly, robustly and accurately analyze images that are used to create patient-specific models. These models form the basis for physiological simulations that are used to determine treatment. Help us bring big data to medical imaging with a strong impact on patient care!
Application Instructions: Please contact Leo Grady (firstname.lastname@example.org) if you are interested in this opportunity or have any questions.