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Mathjobs.org | about 1 month ago
discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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chemical engineering. Areas of emphasis may include, but are not limited to, data-driven process modeling and simulation of complex, large-scale systems; AI-accelerated materials design, including
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multimaterial LPBF, generating process, material, and structural data to support model development, validation, and process consolidation. Develop and apply physics-based and data-driven modeling frameworks to
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quantification Extend the modeling framework to porous and cellular materials, such as polymer and metal foams, including compressible constitutive formulations, calibration against experimental data, and
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-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross
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quantitative methods. Project topics may include statistical modeling, machine learning, optimization, data analysis, mathematical modeling, AI-related applications, financial modeling, healthcare
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statistical modeling, machine learning, optimization, data analysis, mathematical modeling, AI-related applications, financial modeling, healthcare analytics, operations research, or other applied
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design of energy hardware, predictive maintenance of energy infrastructure, and data-driven modeling of fluid and thermal systems. About KAUST and the PSE Division KAUST is an international graduate
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development of redox-active materials for electrochemical CO 2 capture and conversion. Computational modeling and data-driven analysis will be used to guide material selection, molecular design, and