Posted
Fri., Jan. 27
Organization
The Singh Lab of the Columbia University Department of Psychiatry
Position Description
The Singh Lab at Columbia University is looking for a motivated post-baccalaureate student to work as a computational research associate to contribute to method development and analysis in statistical genetics of brain disorders.
Responsibilities include:
- Applying statistical methods to analyze genetic and phenotypic data from clinical collections and population biobanks that include tens to hundreds of thousands of individuals
- Using the depth and diversity of multimodal datasets to characterize the effects of genetic risk factors for psychiatric and neurodevelopmental disorders
- Analyzing 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
- Applying statistical, computational, and machine learning methods for analyzing large-scale genetic and phenotypic data using scalable technologies
- Implementing, documenting, and scaling these methods using robust programming tools and practices for internal use and for sharing with the community
- Contributing to the preparation of manuscripts and subsequent submission to academic journals
- Presenting regularly at internal meetings at NYGC and Columbia
- Actively participating in lab activities
- Sharing expertise and provide training and guidance to new group members as needed
Position Requirements
- B.A. in biological sciences, genetics, statistics, computer science, or equivalent
- Familiarity in at least one modern programming language, including Python, R/RStudio, Java, C/C++, or equivalent
- Familiarity with Unix/Linux platforms, such as basic shell scripting
- Familiarity or interest in applying software engineering best practices (e.g., GitHub for version control, Docker for reproducible environments, etc.)
- Interest in learning about and analyzing high-throughput sequencing data (especially human genetic and functional data)
- Ability to work independently and collaboratively, and strong written and verbal communication skills.
Time Commitment
Full-time, with a two-year commitment. Hybrid work arrangement with at least two days per week in-person.
Compensation
$58-63K based on knowledge, skills, and experience. Benefits included.
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