Pre-doctoral research in computational thermodynamics and kinetics for metallic alloy design and process optimization


Doctoral thesis focused on computational thermodynamics and kinetics of metallic alloys. CalPhaD-based calculations, predicting microstructural features, will be used to efficiently explore alloy compositions and process parameters. The generated data will be used to optimize alloys and processes and to train machine learning algorithms. This is part of a large-scale collaborative project focused on leveraging artificial intelligence for the sustainable design of efficient alloys and processes and will thus require close teamwork with several academic and industrial partners.

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Ref. num. 2024-DT-R1-136

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