The newly established Foundations of Interdisciplinary Data Science Institute (FIDS) at Texas A&M University is searching for two post-doctoral researchers to develop novel machine learning, statistical and computational methods for the analysis of complex and high-dimensional data. This Institute will bring together researchers from five disciplinary areas (Computer Science, Electrical Engineering, Industrial Engineering, Mathematics, Statistics and Industrial engineering) to conduct research on the foundations of data science and to solve problems arising in the fields of Bioinformatics, Energy Research, Manufacturing and Material Science. The post-doctoral researchers will have opportunities to work with researchers from various related disciplines on cutting edge problems; refer to the FIDS website for a complete list of investigators.
We seek highly motivated individuals with a Ph.D. in a quantitative field: Statistics, Machine Learning, Computer Science and Engineering, or a related field. One of the positions will require expertise in Bayesian theory, methods, and computation. The other position will require strong training in optimization and asymptotic theory with application to signal processing or machine learning. The candidates are expected to have strong programming skills, in particular R/Python/Matlab and preferably one lower-level language such as C; and should be interested in the development and application of state-of-the-art statistical and machine learning methods to complex data.
Texas A&M University is committed to enriching the learning and working environment for all visitors, students, faculty, and staff by promoting a culture that embraces inclusion, diversity, equity, and accountability. Diverse perspectives, talents, and identities are vital to accomplishing our mission and living our core values.
The Texas A&M System is an Equal Opportunity/Affirmative Action/Veterans/Disability Employer committed to diversity. Texas A&M University and the FIDS Institute are dedicated to the goal of building an inclusive and culturally diverse researchers who are working in an environment of academic freedom and equality of opportunity.