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Postdoctoral researcher (causal network inference in cancer), Knowles & Azizi Labs - New York New York
Company: New York Genome Center Location: New York, New York
Posted On: 05/04/2024
The Knowles and Azizi labs are seeking a joint postdoctoral research scientist with a background in computational biology, machine learning, and statistics, as well as preferably genomics and cancer biology. This collaborative project combines causal inference and gene regulatory network modeling to better understand cancer progression, metastasis and drug resistance. This project is part of the MacMillan Center for the Study of the Non-Coding Cancer Genome (), which is hosted at NYGC. The incumbent Postdoctoral Research Associate will support the "Inferring cell-type specific gene regulation and the causal networks underlying cancer progression and drug resistance" project as part of the MacMillan Center for the Study of the Non-Coding Cancer Genome (CSNCG). The MacMillan CSNCG is a partnership between The MacMillan Family Foundation and the New York Genome Center. The mission of the CSNCG is to dramatically advance our understanding of human physiology and disease mechanisms in cancer through an unprecedented research program focused on understanding the role and function of the non-coding genome and epigenome in cancer evolution, progression, and treatment response. This requires shared innovative technologies and mathematical approaches, and an interdisciplinary effort between theory, measurement, and application. Thus, a key goal of the CSNCG will be the creation of a common causality discovery platform that brings together engineers, data scientists, mathematicians, technologists, and biological experts to engage in collaborative efforts to develop and scale access to tissue-based measurements and computational tools, which will provide quantification of phenotypes as a function of natural or experimental variation of the dark genome and epigenome. The Knowles lab () develops machine learning methods to understand the role of transcriptomic dysregulation across the spectrum from rare to common genetic disease. This includes characterization of the genetic and environmental factors contributing to mRNA expression and splicing variation. Beyond this specific project there are opportunities for close collaboration with diverse research groups at NYGC collecting large-scale genomics datasets and developing novel genomic technologies including single cell methods, forward genetic screens and long-read transcriptomics. The lab is joint between the NYGC and Columbia University Departments of Computer Science and Systems Biology The Azizi lab () utilizes an interdisciplinary approach combining cutting-edge single-cell genomic and imaging technologies with statistical machine learning techniques, to characterize complex populations of interacting cells in the tumor microenvironment as well as their dysregulated circuitry and spatial organization. The Azizi lab is primarily affiliated with the Biomedical Engineering Department and the Irving Institute for Cancer Dynamics (IICD) at Columbia University. We are also affiliated with the Computer Science Department, Data Science Institute and the Herbert Irving Comprehensive Cancer Center. The lab is located in the Columbia Morningside (main) campus. Key Responsibilities Conceptualize novel computational approaches for causal gene regulatory network inference. Implement and document these methods following good software engineering practices. Benchmark these methods against existing baseline approaches e.g. using simulations. Apply these methods to diverse cancer omics datasets, especially single cell. Document and present progress and results in written or oral reports to other lab members. Prepare manuscripts and external presentations including for center meetings. Building standardized Python and conda packages. Position Requirements |
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