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Multiple data scientist positions in computational biomedicine

Organization

Icahn School of Medicine at Mount Sinai

Job Location

1 Gustave L Levy Place, Box 1498
New York, NY 10029
United States

Job Description

Several NIH-funded data scientist positions are available in Prof. Gaurav Pandey’s (http://research.mssm.edu/gpandey/) lab at the Icahn School of Medicine at Mount Sinai in New York City. The overall project for these positions is the design and implementation of novel machine/deep learning algorithms to predict disease phenotypes from a variety and large amounts of clinical data, such as audio recordings, omic profiles, structured EHR data and clinical notes. The responsibilities of these positions include preparing robust implementation of these algorithms in a big data environment, especially large high-performance computing clusters. This work will be conducted in close collaboration with several prominent collaborators at Mount Sinai and beyond.

The selected candidates will be able to contribute to the ongoing projects in the lab, as well as define their own projects.

Requirements

Candidates should have a Masters or PhD-level degree in any computationally-oriented field and should have a solid background in programming and computational techniques. They should have strong interest in participating in research in big data and computational biomedicine.

How to Apply

To apply, send a CV, an experience statement and three reference letters to [email protected].

About Our Organization

The Pandey lab focuses broadly on developing and applying computational methods to build network and predictive models of complex biological processes and diseases from large biomedical data sets. Lab members routinely analyze large biomedical data sets to build accurate models of biological processes and complex diseases, such as cancer, type-2 diabetes and Alzheimer's disease. Being positioned within a prominent medical center such as Mount Sinai makes it possible to bring the predictions and therapeutic discoveries from these models to patients' bedside, thus placing the lab in a unique position.

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