Job Description:

　　The Singh lab () is looking for a motivated and creative postdoctoral research associate to lead method development and analysis in statistical human genetics, with a focus on brain disorders. The candidate will develop and apply methods to analyze genetic and phenotypic data from clinical collections and population biobanks that include tens to hundreds of thousands of individuals. The candidate will use the depth and diversity of these multimodal datasets to characterize the effects of genetic risk factors for psychiatric and neurodevelopmental disorders. Specifically, they will co-analyze common, rare, and structural variants from whole-genome sequence data and detailed phenotypic (questionnaire, clinical, and imaging) data using novel and scalable methods in statistical genetics and machine learning to understand the pathogenesis of brain disorders. We want to use these insights to identify genes associated with increased risk of particular symptom clusters and pinpoint pathways and processes associated with disease biology.

　　Duties:

　　Develop and apply new statistical, computational, and machine learning methods for analyzing large-scale genetic and phenotypic data using scalable technologiesImplement, document, and scale these methods using robust programming tools and practices for internal use and for sharing with the communityLead planned research projects from start to finishWork closely with computational and clinical colleagues at Columbia and NYGC to interpret and contextualize results from analysisLead the preparation of manuscripts and subsequent submission to academic journalsPresent regularly at internal meetings at Columbia and NYGC and domestic and international scientific conferencesActively participate in lab activities and assist in the mentoring and coordination of junior members of the teamShare expertise and provide training and guidance to group members as needed.

　　Qualifications:

　　Ph.D. in Biological Sciences, Genetics, Statistics, Biostatistics, Computational Biology, Bioinformatics, Computer Science, Epidemiology, or equivalent

　　Preferred qualification:

　　Proficiency in at least one modern programming language, including Python, Java, C/C++, or equivalentProficiency in R/RStudio and relevant statistical packages for data visualization and analysisProficiency in Unix/Linux platforms, such as basic shell scripting in an on-premises or Cloud computing setting (e.g., Google Cloud)Familiarity with analyzing high-throughput genetic data is preferred (e.g., processing FASTA, BAM/CRAM, VCF, BED/BIM/FAM files using PLINK, bcftools, Hail)Proficiency in building robust pipelines for the analysis of genomics or other -omics data (including WGS, RNA-seq, ATAC-seq, etc.)Proficiency in applying software engineering best practices (e.g., GitHub for version control, Docker for reproducible environments, etc.)Columbia University is an Equal Opportunity Employer / Disability / Veteran

　　Pay Transparency Disclosure

　　The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.

　　Equal Opportunity Employer / Disability / Veteran

　　Columbia University is committed to the hiring of qualified local residents.

　　Minimum Salary: 31200.00Maximum Salary: 31200.00Salary Unit: Yearly