Dr. Curtis Huttenhower in the Biostatistics Department of the Harvard School of Public Health seeks a postdoctoral fellow. The successful candidate will be responsible for funded research projects including methods development for high-dimensional multivariate phenotype association and applications to metagenomic biomarker discovery. The Huttenhower lab is broadly engaged in multiple collaborative studies of the roles of the human microbiome in health and disease, with a focus on computational methods to characterize biomolecular functions within these microbial communities and their interactions with host immunity and genetics.
The postdoctoral fellow will perform quantitative methods development for the analysis of structured high-dimensional phenotypic, genetic, genomic, and metagenomic data. Specifically, computational methods are required to control false discovery rates when integrating multiple genetic (e.g., polymorphisms), genomic (e.g., gene expression), metagenomic, and phenotypic (e.g., disease status, biometrics, demographics, diet) data types. These methods will in turn be applied to the integration of multiple studies of the structure and function of the human microbiome in order to determine its causal and correlative associations with disease phenotypes. The candidate will be responsible for both computational methods development and these initial applications, should be broadly conversant with bioinformatic techniques for genomic data analysis, and will be supported by programming staff for the implementation of developed methods as publicly available research software.
The position will be located within the Department of Biostatistics at the Harvard School of Public Health; the Huttenhower group works closely with the Broad Institute, the Human Microbiome Project, the Dana-Farber Cancer Institute, and the broader Boston biomedical and life sciences communities, resulting in a rich environment for quantitative, computational, and laboratory collaborations.
Doctoral degree in Computer Science, Bioinformatics, Biostatistics, Biology, or a related field; familiarity with functional genetic and/or genomic data, as indicated by publication record; proficiency in one or more statistical or scripting languages appropriate for scalable data analysis; ability to communicate scientific material and collaborate well.