The Associate Research Scientist will work primarily on an NIH-funded multi-center project on Biomarkers of Alzheimer's Disease (AD) in Down Syndrome, but will also work on other AD-related projects. This project generates genomic, transcriptomic, proteomic, metabolomic, imaging (MRI and PET), neuropsychological data, and AD status. Using rich, high dimensional data, s/he will engage in research to identify and characterize genetic contributions as well as molecular pathways toward cognitive decline, Alzheimer's disease, and other age-related neurodegenerative diseases in this high-risk population. To better understand the functional genomics of the genes identified, s/he will collaborate with other team members who work on other gene mapping projects, including the Genetic Epidemiology of Alzheimer's Disease in PSEN1 carrier families as well as the Long Life Family Study (a genetic study of healthy aging). In addition, s/he will work with external collaborators with a wide range of expertise (e.g., statistical genetics/genetic epidemiology, neurology, bioinformatics, statistics, cell biology, etc.).
Responsibilities
Use bioinformatics tools to analyze the relations between Alzheimer's Disease related outcomes and high-throughput genomic as well as other omic datasets, including transcriptomic, proteomic and metabolomic datasets;
Responsible for assembling, executing, and developing the latest bioinformatics pipelines for QA/QC and analysis of above omic data;
Perform analyses to examine how the above multi-omics data are associated with imaging data generated from MRI and PET scans, and eventually with neuropsychological and clinical data;
Contribute to the development of bioinformatics and analysis workflows to answer targeted questions about molecular profiles of AD and related phenotypes;
Develop and implement methods for integrative analysis of available multi-omics datasets mentioned above;
Perform statistical analysis to examine genotype-phenotype relations using data that were generated from family-based, case-control, or cohort studies;
Produce subsets of data for distribution to collaborators and/or data repositories as approved by the principal investigators;
Generate manuscripts, grant proposals, and scientific meetings presentations; and
Work independently and collaboratively with highly interactive research groups.
Minimum Qualifications
PhD in Computational Biology, Bioinformatics, Biostatistics/Statistics, Genetic Epidemiology or other relevant computational disciplines;
Proficient in shell scripting and programming proficiency in R, Python, C, C++, or Java on UNIX/Linux environments, particularly with large data files in various formats;
Experience in analysis of human sequence data using bioinformatics tools (e.g., SAMtools, GATK, Picard, STAR, RSEM) and omics databases (Ensembl, 1000 Genomes, GnomAD, GTex, HapMap, ENCODE, etc.); and
Excellent written and verbal communication skills.
Preferred Qualifications
Working knowledge of statistical genetics;
Ability to analyze data on the cloud (e.g., AWS, Google or Azure)
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.
Columbia University is one of the world's most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis. It seeks to attract a diverse and international faculty and student body, to support research and teaching on global issues, and to create academic relationships with many countries and regions. It expects all areas of the university to advance knowledge and learning at the highest level and to convey the products of its efforts to the world.
