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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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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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of Quantitative Finance and Financial Risk Analytics. Areas of interest include, but are not limited to, asset pricing, financial econometrics, financial machine learning, portfolio optimization
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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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, 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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(QBioS), and Machine Learning (ML@GT). Equal Employment OpportunityThe Georgia Institute of Technology (Georgia Tech) is an Equal Employment Opportunity Employer. The Institute is committed to
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online through www.mathjobs.org. Direct all inquiries to mathjobs@math.gatech.edu. The Georgia Tech official website for applications is https://hr.gatech.edu/careers. Some candidates who do not apply
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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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Mathjobs.org | about 1 month ago
an outstanding mathematician whose research engages substantially with artificial intelligence, data science, or scientific machine learning. The successful candidate will have a primary appointment
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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