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.).
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.
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.
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
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