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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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areas: infrastructure systems modeling, emissions inventory development, air quality modeling (chemical transport models and/or reduced-complexity models), exposure assessment of airborne pollutants, and
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workflows in complex organizational settings. Qualifications: Applicants must have a PhD in Computer Science or related field. Experience in one or more ML domains, such as deep learning, reinforcement
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to develop novel enabling technology on hydrodynamic stability analysis that has applications in the study of the development of singularities, the long-time behavior of complex systems, the formation