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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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> on research projects related to kinetic equation and its related multiscale model reduction, numerical method, and data-driven/machine-learning approaches, including: • Numerical simulation of kinetic
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. Bioinformatics analysis of data will include use of population genomics techniques, phylogenetics and machine-learning development. The successful candidate will be expected to write up results as manuscripts
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hydrological and related datasets • Apply machine learning and AI techniques to interpret water system dynamics • Develop and implement algorithms using MATLAB and other programming languages (e.g
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faculty position in the Department of Mechanical Engineering (ME) in scientific machine learning (SciML) and computational modeling of complex multiphysics and multiscale systems. We encourage applications
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Learning, Optimization, Scientific Computing, Statistical Data Analysis, and Topological Data Analysis. In conjunction with these research activities, the CMSE department boasts a strong PhD program and a
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Experience with multivariate ecological statistics or machine learning and GWA Experience with Field research in agronomy or environmental microbiology Experience with gnotobiotic or controlled
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for risk prediction and treatment optimization, AI for image reconstruction, segmentation, and disease detection; application of machine learning and computational methods toward biomaterial, drug, or
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to
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computation, machine learning, networking, formal methods, and software engineering. The Department is home to 55 tenure-system faculty members and teaching specialists, including four endowed professors