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deep learning, preferably including some exposure to graph neural networks or geometric deep learning. Proven experience with implementing machine learning methods in Python and Pytorch. Familiarity with
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user