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, or applications. (2) Proven engineering expertise: Hands-on experience transforming the theory into scalable production systems, backed by patented innovations. (3) A proven research record in mathematical AI (e.g
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mathematics or a closely related field is strongly preferred by the date of appointment. Applicants should have expertise in geometry, topology, and/or number theory and a demonstrated commitment to and
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be expected to seek external funding. The Department has a strong PhD program in Statistics, Graph Theory, Combinatorics, and Applied Mathematics, with very active research collaborations with other
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tools from remote sensing, Geographic Information Science (GIS), graph theory, and data science to address complex research questions. Analyzes both aspatial (e.g., tabular) and spatial (vector and raster
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tools from remote sensing, Geographic Information Science (GIS), graph theory, and data science to address complex research questions. Analyzes both aspatial (e.g., tabular) and spatial (vector and raster
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differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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has a strong PhD program in Applied Mathematics, Applied Statistics, Graph Theory/Combinatorics, and Analysis, featuring active research collaborations both within the University and with external
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: Enrollment in a Master's or PhD program for Mathematics or related field Familiarity with math software/tools (e.g., MATLAB, graphing calculators, Microsoft Excel, R) Additional Information: This is a part
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probability, random matrix theory, random graphs, stochastic analysis, and stochastic processes. Preference will be given to applicants with demonstrated research interests or expertise in the development
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal