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Position TitleVisiting Assistant Professor
The Texas A&M System is an Equal Opportunity/Affirmative Action/Veterans/Disability Employer committed to diversity. The university is dedicated to the goal of building a culturally diverse and pluralistic faculty and staff committed to teaching and working in a multicultural environment and strongly encourages applications from women, minorities, individuals with disabilities and veterans. Texas A&M University has a partner placement program and is responsive to the particular needs of dual career couples. The Department of Statistics is interested in candidates who can contribute to the diversity of the academic community through their research, teaching and/or service.
We are looking for a visiting Assistant Professor at Department of Statistics, Texas A&M University. The research work will be supervised by Dr. Bani K. Mallick. The other investigators in the project are Dr. Veera Baladandayuthapani, Dr. Raymond Carroll and Dr. Han Liang. The primary research focus will be applying novel statistical and computational methods to the analysis of complex, high-dimensional data such as various types of high-throughput proteomic, genomic, sequencing data, with particular emphasis on developing integrative and flexible models that incorporate both biological knowledge and empirical structures. To help the researcher to develop an academic career, there will be opportunities to independently teach two major statistics courses.
We seek a highly motivated individual with a Ph.D. in a quantitative field: Statistics, Biostatistics, computer science, engineering, genomics, bioinformatics, or a related field. Must have strong training in statistics as well as programming skills, in particular R/Matlab/Python and preferably one lower-level computer language such as C or Fortran; and interest in the application of state-of-the-art statistical methods to complex data. Interest or background in bioinformatics, genomics, proteomics is a plus. Expertise or skills in any of the following areas is desirable: analysis of high-dimensional data, Bayesian modeling and computations, Bayesian nonparametrics, graphical models and multivariate techniques, computational biology, data mining, and/or machine learning.