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Postdoctoral Associate
Company Information
Post-doc positions in multi-omics of Alzheimer's disease and cardiometabolic disorders at University of Florida
We are seeking a highly motivated postdoctoral candidate(s) with formal training in Statistical Genetics, Bioinformatics, Data Science, or Genetic Epidemiology. The position is immediately available to work on several NIH-funded research projects on biomarkers of Alzheimer’s disease and cardiometabolic disorders. These projects have generated high-dimensional omics data, including genomic (WGS), epigenomic (EPIC arrays, WGMS), transcriptomic (RNA-seq), lipidomic, metabolomic, and metagenomic data in large-scale human populations. Successful candidate will lead one or more research projects and conduct integrated multiomic analysis using state-of-the-art statistical, bioinformatic and machine learning approaches to identify biomarkers and molecular pathways underlying cognitive decline, Alzheimer’s disease, cardiometabolic diseases (e.g., type 2 diabetes, cardiovascular disease), and other age-related disorders. The candidate will also collaborate with other team members who work on multiomics of these disorders. In addition, the candidate has opportunities to work with faculty members with a wide range of expertise (e.g., statistical genetics, genetic epidemiology, biostatistics, bioinformatics, functional genomics, and translational studies).
• Use bioinformatics and machine learning tools to analyze high-throughput omic data, including WGS, GWAS, DNA methylation, RNA-seq, lipidomics, gut microbiome data, etc.
• Develop and implement methods for integrative analysis of available multiomic data, including genomic, epigenomic, transcriptomic, lipidomic, proteomic, and metabolomic data.
• Perform analyses to examine how multiomic markers are associated with Alzheimer’s disease, diabetes, and cardiovascular disease.
• Prepare manuscripts, grant proposals, and scientific meetings presentations.
• Work independently and collaboratively with highly interactive research groups.
• PhD in Bioinformatics, Biostatistics/Statistics, Statistical Genetics, or Genetic Epidemiology.
• Working knowledge of programming proficiency in R or Python and shell scripting on UNIX/Linux environments, particularly with large data files in various formats.
• Experience in analysis of human sequence data using bioinformatics tools (e.g., trimmomatic, bowtie, SAMtools, GATK, Picard, HOMER) and genetic databases (Ensembl Genes, 1000 Genomes, ESP, HapMap).
• Working knowledge of statistical genetics will be a plus.
• Excellent written and verbal communication skills.