If interested in this position contact Dr. Molly Hall email@example.com with CV and cover letter.
The Hall Lab is seeking highly motivated individuals with a background in Human Genetics, Genetic Epidemiology, Statistical Genetics, Population Genetics, Human Development, Metabolomics, Environmental Science, and/or Bioinformatics for postdoctoral positions at The Pennsylvania State University. Research in our group is highly collaborative, with broad research opportunities for exploring the genetic and environmental architecture of complex traits in diseases such as cancer, diabetes, cardiovascular disease, neurodevelopmental/psychiatric disorders, pharmacogenomic traits, as well as health disparities in childhood. The approaches we explore involve the development and application of statistical and computational methods with a focus on the detection of gene-gene interactions, gene-environment interactions, deep phenotyping with advanced imaging and image analytics, integrating genomic and metabolomics data, and network and/or pathway effects associated with human disease. Skills and abilities: The ideal candidate will have a desire to work within a team and will have strong scientific written, verbal, and electronic communication skills, such as manuscript writing and scientific presentation skills. Additionally, candidates must have: Experience manipulating large-scale data (e.g. genetic, environment, metabolomics); Familiarity with bioinformatics tools and databases for the analysis of results; Record of peer-reviewed publications; Statistical analysis experience using R, SAS, or STATA required and programming abilities such as python or perl are desired. Education and Experience: Ph.D. degree in the area of Genetic Epidemiology, Human Genetics, Statistical Genetics, Biology, Biostatistics, Mathematics, Population Genetics, Human Development, Metabolomics, and/or Bioinformatics or M.D with appropriate research experience required. The Hall Lab at Penn State University is focused on building tools to elucidate the complex genetic and environmental underpinnings of human disease. We integrate genetic and exposure (derived from surveys and metabolomics methods) big data to predict disease status. The ultimate goals of this work are to enrich our understanding of the complex mechanisms that lead to common disease and provide methods to identify those most at risk of disease (based on their genetic and exposure backgrounds) in a clinical setting.