Postdoc: Data scientist position in biomedical machine learning and high-performance computing
A data scientist position is available in the laboratory of Prof. Gaurav Pandey (http://research.mssm.edu/gpandey/) at the Icahn School of Medicine at Mount Sinai in New York City. The target project for this position is the design and implementation of novel machine learning algorithms to build network and predictive models of diseases and biological processes from large complex biomedical data sets. The responsibilities of the position include preparing robust implementation of such algorithms in a big data environment, especially large high-performance computing clusters. This work will be conducted in close collaboration with several other computational and experimental biologists at Mount Sinai and beyond.
The Pandey lab is a part of the recently formed Icahn Institute of Data Science and Genomic Technology (http://datascience.icahn.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. Being positioned within a prominent medical center such as Mount Sinai makes it feasible to bring the predictions and therapeutic discoveries from these models to the patients' bedside, thus placing the institute in a very unique position.
The selected candidate will be able to contribute to the ongoing projects in the lab and the Institute, as well as define his/her own projects.
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. He/she should have strong interest in participating in research in big data and computational biology.
As soon as possible