11/18/2012
University of Texas School of Public Health
Company Information: Fast and cheaper next generation sequencing (NGS) technologies will generate unprecedentedly massive (thousands or even ten thousands of individuals) and highly-dimensional (ten or even dozens of millions ) genomic and epigenomic variation data that allow nearly complete evaluation of genomic and epigenomic variation including common and rare variants, insertion/deletion, CNVs, mRNA by sequencing (RNA-seq), microRNA by sequencing (mRNA-seq), methylation by sequencing (methylation-seq) and Chip-seq. Analysis of these extremely big and diverse types of data sets provide powerful tools to comprehensively understand the genome and epigenomes, but also pose great conceptual, analytical and computational challenges. A deluge of genomic and epigenomic data generated by NGS and enormous amounts of personal phenotype data demand the paradigm shift in genomic and epigenomic data analysis from standard multivariate data analysis to functional data analysis, from low dimensional data analysis to high dimensional data analysis, from independent sampling to dependent sampling, from single type data analysis to integrated multiple types of data analysis, and from individual PC to parallel computing.

Position Title: Postdoctoral Training in Biostatistics, Statistical Genetics, Bioinformatics and Computational Systems Biology.

Duties & Responsibilities: We seek a postdoctoral fellow to engage in a multi-year project who will collaboratively develop and apply novel functional data analysis, data reduction and nonparametric methods to attack current problems in sequence-based genome-wide-association studies, interaction analysis, causal inference and integrated analysis of genetic and epigenetic with Faculty in the Human Genetics Center and Division of Biostatistics.

Position Qualifications: The candidate should have Strong initiative and a Ph.D., or equivalent doctorate in statistics, genetic epidemiology, bioinformatics, computer science, mathematics and any field of engineering. The following qualities are desirable but not required: expertise in one of the following areas: (1) functional data analysis, (2) data reduction, (3) data mining and (4)nonparametric methods; a working knowledge of biology; familiarly with R, Matlab, C and Python.

Salary Range: competitive

Benefits:

Web Site: https://sph.uth.edu/hgc/faculty/xiong/; https://sph.uth.edu/divisions/biostatistics/

Application Address: To apply, send a CV and research statements to Momiao.Xiong@uth.tmc.edu, with “Postdoctoral Fellowship”. All applicants are thanked in advance; only those selected for further consideration will receive a response.

Contact Email: Momiao.Xiong@uth.tmc.edu

Application Deadline: 03/31/2013