POSTDOCTORAL POSITION: The Gu Lab seeks to train a postdoctoral fellow interested in new minimally invasive approaches for molecular diagnostics with a specific focus on methylation profiling of large datasets. The ideal candidate will have a sufficient quantitative and computational background to analyze sequencing and high dimensionality data. Expertise with genome-wide methylation datasets, next-generation sequencing, related molecular experimental techniques, and biostatistics will be helpful. The candidate will have the chance to gain experience within molecular diagnostics, emphasizing ‘liquid biopsy’ cell-free DNA biomarkers, and next-generation sequencing wet and dry lab techniques.
The Gu Lab is a multidisciplinary team that focuses on developing and translating new molecular technologies to advance less invasive and early diagnostics. We are funded by NIH, Burroughs-Wellcome, and the Stanford Department of Pathology. The lab is in the same building and is closely affiliated with the clinical Molecular Genetic Pathology and Clinical Genomics Laboratories. Dr. Gu is a board-certified Molecular Pathologist in these laboratories. We use Novaseq, automation, and a large bank of biospecimens to develop cutting-edge diagnostic technologies and perform studies.
For more information, please visit: cfna.stanford.edu
- Ph.D. or MD/Ph.D. degree prior to starting.
- Preference for expertise with next-generation sequencing or epigenetics with exposure to both the wet lab and dry lab.
- Preference for strong computational/mathematical skills with experience working with large, high dimensionality sequencing datasets, and biostatistics.
- Preference for fluency in shell, python, and R, and a familiarity with standard software packages related to next-generation sequencing, genomics, and epigenetics.
- Track record of productive research through peer-reviewed publications.
- A self-starter, team player, with an eagerness to quickly learn new fields.
- 1-page max Cover Letter on your specific interest in this position and the relevant background
- CV (3-page max)
- Links or Pubmed ID to the most crucial publications where you were a primary contributor
- References: 3 references + contact information