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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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. This includes conceptualizing research questions, designing empirical studies, collecting or acquiring new data sets, and analyzing these data using advanced statistical models and visualization techniques
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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Reality to elicit users’ preferences for innovative transport systems. Applicants with a background in behavioral analysis and mathematical modelling are encouraged to apply. Terms of employment include
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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networks Game-theoretic approaches to cybersecurity and security games Integration of human behavior modeling in smart mobility systems Required Qualifications: The requirements for the applicants include
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Diogo Arsénio on subjects including: Magnetohydrodynamics and the mathematical theory of plasmas. Collisional kinetic models and their hydrodynamic limits. Dispersive phenomena in kinetic theory. Quantum
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-Doctoral Associate to work on a fascinating project focused on the geomechanical modeling for energy applications. The position will be directly supervised by Professor Mostafa Mobasher and will involve
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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing
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generative AI models to establish structure-property relationships for materials discovery. This individual will be expected to collaborate closely with the project team and coordinate efforts with