We are seeking a highly motivated postdoctoral candidate with formal training in Bioinformatics, Statistical Genetics, Data Science, or Genetic Epidemiology. A postdoctoral position is immediately available to work on multiple NIH-funded projects on the Genetic Epidemiology of Alzheimer's Disease. Projects include: (1) a large scale Mexican-American cohort; (2) families of early onset Alzheimer's Disease; (3) adults with Down Syndrome; and (4) families with exceptional longevity. These projects will generate a set of genomic, (epigenomic in some datasets), transcriptomic, proteomic, metabolomic/lipidomic, imaging (MRI and PET), neuropsychological data, and Alzheimer's Disease status. Using these rich, high dimensional multi-omic and imaging data, the candidate will be engaged in research to identify and characterize genetic contributions as well as pathways toward cognitive decline, Alzheimer's disease and other age-related neurodegenerative diseases. The candidate will work with faculty members with a wide range of expertise (e.g., genetics, epidemiology, bioinformatics, neurology, statistics, cell biology, etc.) and other team members on gene mapping, functional genetics and translational studies.
Use bioinformatics tools to analyze high-throughput omics datasets, including whole genome/exome sequencing, GWAS, and RNA-seq data.
Develop and implement methods for integrative analysis of available multi-omics datasets, including genomics, (epigenomic), transcriptomics, proteomics and metabolomics/lipidomic.
Perform analyses to examine how above multi-omics data contribute to neuropsychological and medical phenotypes.
Perform statistical analysis of the genomic data in family-based as well as case-control or cohort study designs.
Prepare manuscripts, grant proposals, and presentations for scientific meetings.
Work independently and collaboratively with a highly interactive research group.
PhD in Bioinformatics, Statistical Genetics, Biostatistics/Statistics, Genetic Epidemiology or other relevant computational disciplines (for example: Biomedical Informatics).
Working knowledge of 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) and genetic databases (Ensembl Genes, 1000 Genomes, ESP, HapMap).
Working knowledge of statistical genetics will be a plus.
Excellent written and verbal communication.
Qualified candidates should e-mail a cover letter, curriculum vitae, and list of three references to Dr. Joseph Lee at JHL2@cumc.columbia.edu (cc: Dr. Lisa Melilli (LEM13@cumc.columbia.edu) with
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