The Postdoctoral Scholar will conduct ongoing research requiring gene network and multimodal single cell analysis from transcriptome data. The successful applicant will develop and apply statistical/computational methods to analyze whole genome sequencing data to elucidate the genetic basis and molecular mechanisms underlying addiction-related traits. Single cell and/or single nuclei analysis will be used to improve gene module annotation for these analyses. These data are being collected in genetic mouse models to improve our understanding of the molecular genetic mechanisms underlying simple and complex traits related to risk for addiction. We are especially interested in residual phenotypic variation after strong directional selection for drug and alcohol intake. Identification of modifiers of genetic risk could lead to more effective therapeutics.
Activities under this fellowship may include, but will not be limited to (1) development of bioinformatics pipelines for expression data analysis, (2) pipeline application to datasets generated from our laboratory experiments, and (3) direct involvement in data collection. This Postdoctoral Fellow appointment is intended to provide full-time, mentored training toward further career development.
We maintain local and distant collaborations to enhance the scope of our research; thus, the Scholar will have the opportunity to interact and pursue training with researchers with multiple levels of expertise. In addition, we work with cores, including the Gene Profiling Shared Resource, which includes the Massively Parallel Sequencing Shared Resource (MPSSR); and the Bioinformatics and Biotstatistics Core at the Oregon National Primate Research Center. State-of-the-art analytical tools are used e.g. the Illumina NovaSeq 6000. There will also be the opportunity to gain expertise in classical genetics, behavioral analysis, pharmacology and molecular biology.
Salary for this position aligns with the NIH Scale.
Apply here online. Please be sure to upload a cover letter and Resume or CV.