The Genomic Data Modeling lab at Seattle Children’s Research Institute, led by Dr. Mohammadi, seeks highly motivated Postdoc interested in method development to join ongoing systems genetics and precision medicine projects. We use a statistical machine learning approach to address problems in biomedical research, including modeling variation in the non-coding genome, identifying genetic variation that contributes to disease, and analyzing multi-omics data.
Our research is at the interface of Machine Learning and Statistical Genetics. We develop and apply mathematical frameworks, statistical models, and deep learning to address the computational challenges of analyzing genome variation and its effect on human health. We participate in basic research as part of large functional genomics consortia and collaborate closely with medical doctors in the clinic to ensure translational impact.
For more information, visit our lab website: https://pejlab.org or contact Dr. Mohammadi directly via email: pejmanm@uw.edu
The position involves developing theoretically solid and biologically justified models and analysis pipelines for large genomic data. Thus, experience in any of the following areas is of interest: Genetics, Statistical Genetics, NGS data analysis, Bioinformatics, Machine Learning, Probabilistic Modeling, Statistics, Computer Science, and Optimization. Extensive programming experience is necessary for all candidates.
The Genomic Data Modeling lab works at the interface of statistics and genomics. Our offices are located in downtown Seattle at the Center for Immunity and Immunotherapies at the Seattle Children's Research Institute. We are affiliated with the University of Washington School of Medicine and the Department of Genome Sciences and have close collaborations with other Seattle area research institutes, such as the neighboring Fred Hutchinson Cancer Center in downtown Seattle.
Requirements
Required Education/Experience:
- Doctorate degree (PhD/MD) in Genetics, Genomic, Bioinformatics, Computer Science, or Physics, or closely related field.
- Experience using basic research techniques of specialty area.
- Solid scientific programming experience.
Preferred:
- Substantial prior experience in population genomics.
- Strong background in data analysis or modeling
- Experience with Next generation sequencing data analysis
