The Knowles lab (daklab.github.io/) at the New York Genome Center (NYGC) and Columbia University Departments of Computer Science and Systems Biology is seeking a postdoctoral scholar to work on an NIH-funded project using machine learning to understand the genetic underpinnings of Alzheimer’s disease (AD). This multidisciplinary project is a collaboration with Dr. Towfique Raj’s group in the Departments of Neuroscience and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai and will be part of the National Institute on Aging’s Alzheimer’s Disease Sequence Project (ADSP) consortium. The project will involve the development and application of deep learning, network and causal inference methods to large-scale whole genome and transcriptome sequencing data with the aim of identifying the genetic variants, regulatory elements, genes, pathways and cell-types involved in AD pathogenesis.
The Knowles lab aims to understand the role of transcriptomic dysregulation across the spectrum from rare to common genetic disease. This involves better characterization of the genetic and environmental factors contributing to mRNA expression and splicing variation. Beyond the specific project there are opportunities for close collaboration with diverse research groups at NYGC collecting large-scale genomics datasets in the context of neurological disease and developing novel genomic technologies including single cell methods, forward genetic screens and long-read transcriptomics. All lab members are eligible for an appointment at Columbia University (as well as the primary appointment at NYGC).
Please include the following items with your application: 1) CV with a list of publications; 2) a short (maximum one page) summary of your present and future research interests; 3) a list of 2-3 references with contact information.
PhD in machine learning, statistics, statistical genetics or a related quantitative field.
Strong interest in genetics and genomics.
At least one first author publication (or preprint).
Experience with NGS data analysis, functional genomics and/or human genetics preferred.
Good software engineering abilities (e.g. version control, code review, unit testing), or willingness to learn these.