Post-doctoral level Data Scientist position in mining environmental health and genomics data

Organization
Icahn School of Medicine at Mount Sinai
Job Location
New York, NY
Job Description

A post-doctoral level NIH-funded data scientist position is available in the group of Prof. Gaurav Pandey (http://www.mountsinai.org/profiles/gaurav-pandey) at the Icahn School of Medicine at Mount Sinai in New York City. The target project for this position is the development and application of data mining and machine learning algorithms to derive enhanced predictive models and actionable knowledge from environmental health and genomics data. Much (but not all) of these data will be generated through the CHEAR program (http://www.niehs.nih.gov/research/supported/dert/programs/chear/), which aims to revolutionize children’s environmental and public health by promoting data-intensive and big-data research in these areas. The computational techniques developed in this position will be applied to these and other data, and will eventually also be integrated with genetic/genomic data to gain a more complete understanding of the genetics X environment contribution to health and disease. This work will be conducted in close collaboration with the Department of Preventive Medicine at Mount Sinai, which is one of the world leaders in children’s environmental health research and has experts in environmental epidemiology, biostatistics and environmental exposure assessment. This collaboration, as well as being positioned within a prominent medical center, makes it feasible for the acquired knowledge to be incorporated in health policy, community wellness and patient treatment.

Requirements

A post-doctoral level NIH-funded data scientist position is available in the group of Prof. Gaurav Pandey (http://www.mountsinai.org/profiles/gaurav-pandey) at the Icahn School of Medicine at Mount Sinai in New York City. The target project for this position is the development and application of data mining and machine learning algorithms to derive enhanced predictive models and actionable knowledge from environmental health and genomics data. Much (but not all) of these data will be generated through the CHEAR program (http://www.niehs.nih.gov/research/supported/dert/programs/chear/), which aims to revolutionize children’s environmental and public health by promoting data-intensive and big-data research in these areas. The computational techniques developed in this position will be applied to these and other data, and will eventually also be integrated with genetic/genomic data to gain a more complete understanding of the genetics X environment contribution to health and disease. This work will be conducted in close collaboration with the Department of Preventive Medicine at Mount Sinai, which is one of the world leaders in children’s environmental health research and has experts in environmental epidemiology, biostatistics and environmental exposure assessment. This collaboration, as well as being positioned within a prominent medical center, makes it feasible for the acquired knowledge to be incorporated in health policy, community wellness and patient treatment.

About Our Organization

Our group focuses broadly on developing and applying machine learning methods to build computational models of biological processes and disease from large genomics and environmental health data sets. It is a part of the Icahn Institute for Genomics and Multiscale Biology (http://multiscale.mssm.edu) at Mount Sinai. The Institute aims to revolutionize the field of genomic medicine by bringing to the table skills from very unorthodox disciplines (for biomedicine), such as computer science, statistics, physics and high-performance computing. The faculty members of the institute, experts in all these areas, 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. The proposed position aims to connect these models with the influencing environmental factors to gain a more complete understanding of health and disease, which is a recent emphasis area for the institute.   

The selected candidate will be able to contribute to the ongoing projects in the group and the Institute, as well as define his/her own projects.

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