Staff Associate I

We are seeking a Staff Associate I to join our research team focused on Alzheimer's disease (AD) genetics and functional genomics. The successful candidate will be involved in analyzing data from a multi-ancestry, multi-omics QTL (xQTL) project aimed at uncovering genetic regulations and etiologies underlying AD.

Responsibilities:

1. Data Analysis and Integration:

• Conduct comprehensive analyses of whole-genome sequence (WGS), single-nuclei RNA sequencing (snRNAseq), RNAseq, snATACseq, proteomics and other omics data / biomarkers across diverse populations and tissue types.
• Perform advanced QTL mapping to identify and characterize xQTL, including eQTL, pQTL, and other novel QTL types, under cell type, ancestry, and context-specific conditions.

2. Functional Annotation and Quality Control:

• Annotate and interpret genetic variants, focusing on non-coding regions and their regulatory functions in AD-related pathways.
• Implement rigorous quality control measures to ensure the accuracy and reliability of genomic data.

3. Collaborative Research and Methodology Development:

• Collaborate with a multidisciplinary team, including computational biologists, neurologists, and geneticists, to develop and apply novel computational methods for xQTL and GWAS integration.
• Contribute to the development of new analytical approaches to address challenges such as missing heritability and missing regulation in genetic studies.

4. Resource Development and Sharing:

• Participate in the creation and maintenance of a comprehensive xQTL atlas, integrating data from various cohorts and tissue types.
• Engage in collaborative efforts to share data and findings with the broader research community, supporting resource distribution and 'omics data analysis training.

Bachelor's degree in specific research field and 4 years experience preferred.

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.