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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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of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks
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) the development of a holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML