Postdoctoral Scholar – Statistical Diversity Lab

University of Washington • Seattle, WA

5d • Onsite

About The Position

The Department of Biostatistics at the University of Washington has an outstanding opportunity for a Postdoctoral Scholar. The Statistical Diversity Lab, led by Dr. Amy Willis, is one of the most respected international groups developing methods for the analysis of microbiome data. The StatDivLab develops cutting-edge statistical methodology to address the most important methodological needs of microbiome scientists. Software, outreach and training complement their research mission. The StatDivLab is seeking a Postdoctoral Scholar with PhD-level training and research experience in either Statistical Methodology or Microbial Bioinformatics. The research project undertaken by the Postdoctoral Scholar will be tailored to the background and interests of the candidate, with possible projects in methods for emerging experimental designs (e.g. absolute quantification through spike-ins; multi-omic datasets), microbial phylogenetics, variable importance, model selection, trait prediction, and uncertainty quantification. Substantial professional development opportunities are available, including travel and speaking opportunities at international conferences and workshops and engaged mentoring towards long-term professional goals. This is an in-person position based in Seattle, WA. It is a full-time (100% FTE), 12-month/year position, with an initial appointment length of one year and the possibility of renewal.

Requirements

• PhD or equivalent in Statistics, Biostatistics, or a related field (for Statistical Methodology track).
• PhD or equivalent in Computational Biology, Microbiology, or a related field (for Microbial Bioinformatics track).
• At least one published contribution in statistical methodology (for Statistical Methodology track).
• At least one published contribution in computational biology, microbiology, or a related field (for Microbial Bioinformatics track).
• Genuine interest in applying methods to contemporary datasets (for Statistical Methodology track).
• Research experience with shotgun sequencing data, including most of the following: working with public databases, assembly, annotation, binning, SNV profiling, and distributed computing infrastructure (cluster and/or cloud) (for Microbial Bioinformatics track).
• An interest in learning contemporary statistical methods (for Microbial Bioinformatics track).
• Strong written and oral communication skills.
• Strong skills in project management and time management.
• Proficiency in R, python, C++, Java or bash as demonstrated through a record of open-source software distribution.

Nice To Haves

• Experience with microbiome, genomics or tree-valued data (for Statistical Methodology track).
• Familiarity with machine learning methods (for Microbial Bioinformatics track).
• Experience with anvi'o databases (for Microbial Bioinformatics track).
• Multiple publications in the field of the candidate’s expertise.

Responsibilities

• Develop cutting-edge statistical methodology to address the most important methodological needs of microbiome scientists.
• Undertake research projects tailored to the candidate's background and interests, potentially in areas such as methods for emerging experimental designs, microbial phylogenetics, variable importance, model selection, trait prediction, and uncertainty quantification.
• Contribute to software development, outreach, and training activities of the Statistical Diversity Lab.
• Manage projects and time effectively.
• Communicate research findings effectively through written and oral presentations.

Benefits

• Salary commensurate with experience and qualifications, or as mandated by a U.S. Department of Labor prevailing wage determination.
• Benefits included.
• Substantial professional development opportunities, including travel and speaking opportunities at international conferences and workshops.
• Engaged mentoring towards long-term professional goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree