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whose scholarship or teaching connects to the mathematics of artificial intelligence and machine learning. The teaching load is two courses per semester. Bowdoin is particularly interested in candidates
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whose scholarship or teaching connects to the mathematics of artificial intelligence and machine learning. The teaching load is two courses per semester. Bowdoin is particularly interested in candidates
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whose scholarship or teaching connects to the mathematics of artificial intelligence and machine learning. The teaching load is two courses per semester. Bowdoin is particularly interested in candidates
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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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geometry, number theory, data. * Qin Li: Scientific computing, machine learning, inverse problems. * Hanbaek Lyu: Combinatorics, probability, optimization, and machine learning. * Laurentiu Maxim
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in applied mathematics, mathematical physics, and scientific machine learning. More information about the department can be found at https://uwaterloo.ca/applied-mathematics/. Candidates interested in
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Mathjobs.org | about 1 month ago
applications are particularly encouraged to apply. Expertise in statistical or machine-learning methods is also welcome, as connection with experimental and clinical data is important for our work. Prior
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applications are particularly encouraged to apply. Expertise in statistical or machine-learning methods is also welcome, as connection with experimental and clinical data is important for our work. Prior
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PhD degrees, and strong undergraduate programs in applied mathematics, mathematical physics, and scientific machine learning. More information about the department can be found at https://uwaterloo.ca
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. The research area of interest is in applied mathematics with a preferred emphasis in Theoretical and Computational Statistics, Machine Learning, Data Science, and/or Mathematical Molecular Biology. We