Postdoctoral Scholar Research Associate

University of Southern California•Los Angeles, CA

2d•$70,000 - $75,000•Onsite

About The Position

A Postdoctoral Research Associate position in cancer and genetic epidemiology is available at the USC Center for Genetic Epidemiology within the Department of Population and Public Health Sciences at USC. The applicant must have a Ph.D. (or equivalent qualification) with training in epidemiology, biostatistics and/or statistical genetics. The postdoctoral fellow will work directly with Dr. Chris Haiman and Dr. Fei Chen on analyzing risk factors, genetic (e.g. GWAS, whole-genome and whole-exome sequence, copy number variants, LoY, CHIP) and other omics data (e.g. metabolomics, proteomics, methylation, microbiome) from diverse cohorts including the Multiethnic Cohort study, the RESPOND African American prostate cancer study and other large-scale genetics consortia (e.g. NHGRI PAGE and PRIMED). In addition to cancer, non-cancer outcomes will also be examined including anthropometric traits, diabetes, chronic kidney disease and related biomarkers. Studies focused on developing multi-omics risk prediction models, rare and structural variant analysis and health disparities research will be available. Desired is an applicant familiar with many of the most common analytic tools for genetic association studies. In addition, they will participate in the presentation of their research and in the writing of publications. The position requires a highly motivated individual with excellent written and verbal communication skills.

Requirements

Ph.D. (or equivalent qualification) with training in epidemiology, biostatistics and/or statistical genetics.
Familiarity with many of the most common analytic tools for genetic association studies.
Highly motivated individual with excellent written and verbal communication skills.

Nice To Haves

Training in epidemiology, biostatistics and/or statistical genetics
Epidemiology, biostatistics and/or statistical genetics

Responsibilities

Analyze risk factors, genetic data (e.g. GWAS, whole-genome and whole-exome sequence, copy number variants, LoY, CHIP) and other omics data (e.g. metabolomics, proteomics, methylation, microbiome) from diverse cohorts.
Examine non-cancer outcomes including anthropometric traits, diabetes, chronic kidney disease and related biomarkers.
Conduct studies focused on developing multi-omics risk prediction models, rare and structural variant analysis and health disparities research.
Participate in the presentation of their research.
Participate in the writing of publications.