Postdoctoral Research Associate in Statistical Genetics, Singh Lab

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 hope to use these insights to identify genes associated with increased risk of particular symptom clusters and pinpoint pathways and processes associated with disease biology.

We are an interdisciplinary and collaborative team at the New York Genome Center (NYGC), with primary affiliations at the Columbia University’s (CU) Department of Psychiatry and the New York State Psychiatric Institute (NYSPI) and a secondary affiliation at Columbia University’s Zuckerman Mind Brain Behavior Institute. This position offers a unique opportunity to participate in a multi‑institute research environment in New York City, with international travel and collaboration opportunities.

Your work will be central to building and strengthening the neuropsychiatric genetics program at the NYGC and CU Psychiatry. You are encouraged to learn from and engage with other researchers in lecture series, grand rounds, and academic events at these centers and institutes, which include work in statistical genetics, functional genomics, basic neuroscience, and clinical translation. In addition to leading research in our team, you can access resources for professional advancement and skill development at the NYGC and Columbia, which will help you build toward an academic or industry career.

This position is eligible for visa sponsorship and relocation assistance.

• Develop and apply new statistical, computational, and machine learning methods for analyzing large‑scale genetic and phenotypic data using scalable technologies;
• Implement, document, and scale these methods using robust programming tools and practices for internal use and for sharing with the community;
• Lead planned research projects from start to finish;
• Work closely with computational and clinical colleagues at NYGC and Columbia to interpret and contextualize results from analysis;
• Lead the preparation of manuscripts and subsequent submission to academic journals;
• Present regularly at internal meetings at NYGC and Columbia and at domestic and international scientific conferences;
• Actively participate in lab activities and assist in the mentoring and coordination of junior members of the team;
• Share expertise and provide training and guidance to group members as needed.

• Ph.D. in Biological Sciences, Genetics, Statistics, Biostatistics, Computational Biology, Bioinformatics, Computer Science, Epidemiology, or equivalent;
• Proficiency in at least one modern programming language, including Python, Java, C/C++, or equivalent;
• Proficiency in R/RStudio and relevant statistical packages for data visualization and analysis;
• Proficiency 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., Git Hub for version control, Docker for reproducible environments, etc.);
• Ability to work closely with other scientists to product ionize and scale new data analysis methods and workflows;
• Interest and…