Group Leader, Computational Biology, Xavier Lab

Broad Institute • Cambridge, MA, United States • $200k - $250k / year • 15h ago

Description & Requirements

The Infectious Disease and Microbiome Program (IDMP) is looking for a Group Leader in Computational Biology to lead and direct investigations into the mechanisms of host microbiome interactions in health and disease. We are an interdisciplinary group with expertise in computational analysis, microbiology, and immunology. The candidate for this position will be working closely with computational and experimental biologists, graduate students, postdocs, and staff scientists to contribute to directing, planning, developing novel methods, and executing the analysis of large multi-omic datasets with the goal of identifying microbes, genes, or microbial products that are relevant to human health. The ideal candidate should be broadly experienced with analyzing next-generation sequencing data, statistics, and machine learning, and should be willing to collaborate closely with computational postdocs for methods implementation, and with experimental team members for validation of computational results. Prospective applicants should have made significant contributions to their area of study, as evidenced by a strong publication record. We are seeking creative and highly motivated individuals who want to work in a dynamic, multi-disciplinary research environment.

RESPONSIBILITIES

Design and execution of small and large collaborative studies
Track record in developing new methods
Mentoring and supervision of scientists at various levels
Writing of publications, grants, and reports
Maintaining up-to-date toolbox of computational methods

REQUIREMENTS

A Ph.D. in Computational Biology, Bioinformatics, or a related field
*4-5 years postdoctoral experience in computational biology or equivalent industrial experience, preferably with direct involvement in human microbiome research
Experience managing or mentoring a team with demonstrated leadership skills
Proficiency in one or more statistical or scripting languages appropriate for scalable data analysis
Familiarity with functional genetic and/or genomic data, as indicated by publication record
Ability to clearly communicate scientific material and work collaboratively.

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