The Department of Statistics invites applications for a Staff Associate I who will conduct research detecting spatially variable genes which are correlated with tension phenotypes. Applied to spatial transcriptomics datasets, this analysis will help understand the molecular basis of aging phenotypes in patients with Werner syndrome in the lab of Prof Bianca Dumitrascu.
Responsibilities will include:
--(40%): Build and run reproducible Python/R pipelines for spatial transcriptomics data on the lab’s computing cluster, including quality control, normalization, cell segmentation, and detection of spatially variable genes
--(25%): Implement, adapt, and benchmark statistical methods that relate spatial gene expression to tissue tension phenotypes
-- (15%): Acquire and apply working knowledge of spatial genomics, statistical methods, and the biology of aging and Werner syndrome
-- (10%): Prepare figures, written summaries, and documentation; present results at lab meetings; contribute to methods sections of manuscripts
--(10%): Organize and maintain datasets, code repositories, and computing environments
Bachelor's degree in statistics, computer science, mathematics, computational biology, or a related quantitative field.
Python and/or R programming; statistics; data visualization; git and command line.
Prior research experience, including undergraduate research, preferred.
All applications must be made through Columbia University’s Academic Search and Recruiting (ASR) system. Please upload the following required materials: Curriculum vitae.
Columbia University is an Equal Opportunity Employer / Disability / Veteran
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.
