A post-doctoral position in human genomics is immediately available. Research in our group focuses on the development of bioinformatics methods to analyze short-read and long-read sequencing data, and to enable the implementation of genomic medicine in clinical settings. For more information, please check the group website at http://wglab.org.
The successful candidate will work on the methods and applications of novel informatics approaches to handle unmet challenges in genome and exome sequencing data. Example projects include the analysis of large-scale Illumina genome sequencing data, the establishment of automated pipelines for Nanopore sequencing data, the clinical interpretation of genetic mutations in rare diseases, the identification of genetic biomarkers for drug efficacy and adverse events, and the discovery of somatic/expression biomarkers for certain categories of diseases.
The candidate will work in a highly interdisciplinary and collaborative academic environment including the Wang Genomics Lab, the Center for Cellular and Molecular Therapeutics, the Department of Biomedical Informatics at CHOP as well as the Department of Pathology, Department of Genetics and Institute for Biomedical Informatics at the University of Pennsylvania. The position also offers attractive salary, exposure to a variety of educational and collaborative opportunities, as well as an excellent opportunity to publish high impact manuscripts to facilitate career advancement in the future. For additional information about the environment, please check https://www.med.upenn.edu/postdoc/ and https://training.research.chop.edu/.
A suitable candidate should have a doctoral degree in genomics-related fields, such as genetics, bioinformatics, computational biology, biostatistics or computer science, with strong prior experience in human genomics, such as published work in finding human disease genes through genomic approaches, familiarity with computational tools/pipelines for variant detection and annotation, or established web servers for genomic data analysis.