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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 Drs. Jianhua Huang (Arseven/Mitchell Chair in Astronomical Statistics), PR Kumar (College of Engineering Chair, Electrical Engineering) Yu Ding (Mike and Sugar Barnes Professor, Industrial Engineering) and Ibrahim Karaman (Chevron Professor, Material Science). The primary research focus will be applying novel machine learning, statistical and computational methods to the analysis of complex, high-dimensional data from Material science and Windmills. The main objective of this interdisciplinary project is to develop a radically new, multi-fidelity active learning theory, based on combining hitherto disparate knowledge disciplines (data science and control theory) to analyze Big Data. 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, Machine Learning, Computer science, Engineering, or a related field. Must have strong training in statistics, signal Processing or machine learning as well as programming skills, in particular R/Matlab/Python and preferably one lower-level computer language such as C; and interest in the application of state-of-the-art statistical methods to complex data. Some knowledge of Bayesian modeling and computation will be desirable.