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The Johnston Laboratory (https://johnstonlaboratory.com/) in the Vaccine and Infectious Disease Division at the Fred Hutchinson Cancer Research Center is recruiting a highly motivated and creative Post-Doctoral Fellow to conduct research relating to synthetic microbiology and microbial genetics / epigenetics. The successful candidate will be involved with delineation of the innate genetic defense systems of bacterial species and the design, development, and implementation of methodologies to bypass these systems during genetic engineering. This position will be funded under an NIH Director's Transformative Research Award (TR01) research grant entitled “The SyngenicDNA and µPOET Platform: Overcoming Innate Barriers to Genetic Engineering in Bacteria.”
*Please include a cover letter with your application, detailing your interest and suitability for this position.
The Johnston Laboratory is an interdisciplinary and highly collaborative team of classically trained microbiologists, synthetic microbiologists, bioinformaticians and engineers. Using cutting edge technologies and methodologies, we seek to gain a deeper functional understanding of what bacteria are doing and how they are doing it, and then to use that information to create novel therapeutics and engineer the next generation of microbe-based technologies for application in human medicine, synthetic microbiology and bioengineering.
Wet-lab: The ideal post-doctoral candidate must have wet-lab experience in molecular biology and the cultivation/genetic manipulation of non-model bacteria (in addition to laboratory strains of E. coli). Hands-on experience with CRISPR-Cas / Transposon mutagenesis / Recombineering techniques (one or multiple) are essential. Experience with bacterial conjugation is also a plus, but not required.
Dry-lab: The candidate will work closely with a computational informatician in development/curation of a comprehensive database of genetic defense systems in bacteria, and accordingly dry-lab experience with Linux-based bioinformatic software / data analysis environments (Python/R) is favorable. Familiarity with handling DNA/RNA sequencing data and related programs will be advantageous.