Job:Postdoctoral position in Machine Learning/Precision Medicine, Mount Sinai School of Medicine, New York City, USA
Entering edit mode
6.8 years ago

A postdoctoral position in Machine Learning is available at the Mount Sinai School of Medicine. The successful candidate will join ongoing efforts in the Precise MD program, an innovative initiative that leverages our computational pathology infrastructure to integrate machine vision and machine learning algorithms for diagnostic and prognostic assay development. Specifically, the candidate will be working on building prognosis models in prostate and breast cancer, based on the integration of clinical endpoints and quantitative biomarker characteristics derived by an advanced machine vision platform.

Successful candidates will have earned a doctoral degree or foreign equivalent in an interdisciplinary, data-driven field, with educational emphasis in machine learning /statistics/data science preferred; experience in image analysis is a plus. Special consideration will be given to candidates with experience in survival analysis. Additional qualifications include: a superior academic performance, proficiency in Matlab, R, Python etc and an ability to be self-directed with broadly-defined limits on assignments; excellent communication skills, both oral and written; and a demonstrated ability to interact efficiently with diverse people in a highly multidisciplinary environment. This is a full-time, two-year postdoctoral position. The postdoc may not have more than five years of postdoctoral experience including that from other institutions.

Review of applications will begin immediately with the position to be filled as soon as possible. We encourage applications from individuals of diverse backgrounds. Interested individuals should send a CV and the names of three references to Dr. Suarez-Farinas ( with a subject line "postdoc position").

predictive-medicine machine-learning postdoc • 2.2k views

Login before adding your answer.

Traffic: 2099 users visited in the last hour
Help About
Access RSS

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6