Postdoctoral Fellow in Radio-Gen-Pathomics

Memorial Sloan Kettering Cancer Center
New York, NY, USA
Job Type: 
Full Time
Closing Date: 
Friday, December 1, 2017

We are looking for a highly motivated postdoctoral fellow interested in using machine learning techniques to revolutionize the treatment of patients with cancer. The fellow will work with Dr. Fuchs and Dr. Simpson to combine pathomic, radiomic, and genomic data for the quantitative assessment of cancer. The fellow will work with histopathology, computed tomography scans, next generation sequencing, and clinical data to improve the evaluation of response, recurrence, and outcome towards the realization of precision medicine. This position is part of a large multidisciplinary disease management team from the Departments of Surgery, Radiology, Pathology, and Medicine, investigating new computational methods toward precision cancer care. Drs. Fuchs’ and Simpson’s groups are uniquely situated with a wealth of clinical data in support of their novel technological advancements. In particular, Dr. Fuchs’ group has been named Center of Excellence for GPU Computing by NVIDIA.

Applicants should hold a Ph.D. in computer science, engineering, or related discipline with emphasis on image analysis (pathological or radiographic images). Individuals with experience in quantitative imaging, machine learning algorithms (deep learning an asset) for classification, would be excellent additions to our team. Strong programming skills in MATLAB or Python are required.

Please email a cover letter indicating research interests, curriculum vitae including list of publications, and contact information for three referees.


Amber Simpson, Ph.D.
Assistant Attending Computational Biologist
Department of Surgery
Memorial Sloan Kettering Cancer Center