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Department of Statistics at Purdue University invites applications for one full-time postdoctoral fellow to conduct research on developing statistical methods for analyzing omics data.
The postdoctoral fellow will be supervised by Prof. Dabao Zhang (www.stat.purdue.edu/~zhangdb). The project is funded by a NIH R01 grant with investigators from Purdue University and University of Washington at Seattle. The focus of this project is on developing statistical methods to evaluate the stability of metabolic profiling across different platforms, different places, and different lengths of sample storing times. The task involves methods development and applications.
We expect the successful applicants to be the leading author or co-author on both statistical and applied publications, and to disseminate research findings at professional conferences. We seek candidates who are willing to commit to at least two years on the project. We encourage candidates to bring in their own novel research ideas or initiate their own research project in the line.
-- Recent doctoral degree in Statistics, Biostatistics or related fields;
-- Familiar with linear mixed models;
-- Strong statistical computing skill in R;
-- Research experience in metabolic profiling is a plus but not necessary;
-- Excellent writing skills and communication skills.
-- Expertise in the following areas would be useful but not critical: analysis of large-scale omics data, high-dimensional variable selection, and machine learning.