Postdoctoral Fellow for Microbiome Epidemiology and Bioinformatics
School    Harvard T.H. Chan School of Public Health
Department/Area    Biostatistics

Position Description    
The Harvard T.H. Chan School of Public Health Microbiome Analysis Core is seeking a postdoctoral fellow for microbiome epidemiology and bioinformatics. The Microbiome Analysis Core, located in the Department of Biostatistics, supports a comprehensive computational and statistical platform for population studies of the human microbiome, its interaction with health and disease, and methods for data mining and machine learning in multi-omic data. This job will entail work with the Microbiome Analysis Core personnel applying and extending microbiome informatics and statistical methods, developed in the Huttenhower lab (e.g. MetaPhlAn, HUMAnN) as well as standards in the field (DADA2, MEGAHIT), to new human microbiome profiles, including microbial communities assayed in disease, animal models, cross-sectional and prospective human cohorts, and associated clinical phenotypes and/or environmental/lifestyle exposure metadata. These studies generally have the goal of identifying features of the microbiome (16S amplicon, shotgun metagenomic, and shotgun metatranscriptomic sequencing, yielding taxa, gene families, enzymes, and/or pathways) associated with various phenotypes, exposures, and/or outcomes. There will be regular interactions with internal and external contacts, including scientists, collaborators, postdocs, students, and clinicians and industry leaders. Optional mentoring and/or teaching opportunities include lab PhD or Masters students, junior researchers, public guest lectures, and short course workshops.

Basic Qualifications    
Doctoral degree in Biostatistics, Bioinformatics, Computer Science, Computational Biology, Molecular Biology, Biology/Life Sciences, or related fields.
Proficiency in R programming and Linux/Unix command line required.
Preference given to candidates with experience in microbiome analysis, ordination and cluster analysis, sequence analysis, intermediate R programming, Python programming, a background in biostatistics, and computing clusters (e.g. Slurm).
Excellence in research, communication, and collaboration skills, as evidenced by publication record.
Ability to handle a variety of tasks, effectively solve problems with numerous and complex variables, and rapidly shift priorities.
Excellent attention to detail is required.  

Contact Information    
Jeremy Wilkinson

Contact Email    jewilkinson@hsph.harvard.edu

Equal Opportunity Employer    
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required    2 
Maximum Number of References Allowed    4