Postdoctoral Scholar in Statistical Genetics and Computational Genomics
- University of Chicago
- Location: Chicago, IL
- Job Number: 7066895
- Posting Date: Jun 13, 2020
- Application Deadline: Open Until Filled
Job DescriptionApplications are invited for postdoctoral researchers to join Department of Human Genetics of University of Chicago. The candidate will be jointly supervised by Dr. Xin He and Dr. Matthew Stephens. The research in our labs focuses on developing and employing computational tools to analyze genetic and genomic datasets, often in the context of complex human diseases. Our research spans multiple areas including genome-wide association studies (GWAS), regulatory genomics, single cell genomics, whole exome/genome sequencing, and cancer genomics. Our labs provide an exceptional research environment, sharing computational space with the lab of John Novembre. Members of the different labs have a range of computational, statistical and biological backgrounds, and interact on a daily basis. For more information of our research, please visit: http://xinhelab.org and http://stephenslab.uchicago.edu.
Some potential projects include, but are not limited to: (1) Developing better methods to link molecular-level genetic data (e.g. expression QTL, chromatin accessibility QTL) with GWAS of complex traits. (2) Developing methods for statistical fine-mapping of risk variants and risk genes of human diseases. (3) Developing methods for better understanding of how germline and somatic genetic variations drive tumorigenesis.
The applicant(s) is expected to hold a doctoral degree in a related field, such as computational biology and bioinformatics, statistical genetics, (bio)statistics or computer science. Candidates with a degree in biological sciences are also encouraged to apply if they have demonstrated experience in computational or statistical work. The start date for this position is flexible.
Please send your CV and a list of 2-3 references (phone and email addresses) to firstname.lastname@example.org. Candidates are welcome to include one or two of their best published or preprint manuscript(s).