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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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collaboration between multiple research groups led primarily by Professor Tarek Abdoun and Professor Mostafa Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling
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or more of the following: Centrifuge experimental testing (design, execution, and data interpretation) Numerical modeling and simulation to complement physical experiments Preferred) Proficiency in CAD
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and mathematical modelling are encouraged to apply. Terms of employment include competitive salary and benefits. Research in the SIT-D lab focuses on understanding and modelling consumers' behavior and
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interest may be considered, a priority will be given to those able to relate to one or several of the above topics. For more information on the research directions, applicants are encouraged to check
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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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natural language processing and machine learning workflows; (3) experimental design and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal
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. The role will involve designing surveys, working with existing datasets, and applying models of firm dynamics in settings of structural transformation and economic growth. We are interested in identifying
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models related to modern data management challenges (e.g., query feasibility, data correlations, probabilistic evaluation, scalability). Prototype and evaluate data system components or extensions
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(PDA) to engage in cutting-edge research on channel modeling and characterization for communication and sensing in emerging spectrum for 6G and beyond, with a focus on the FR3, THz and optical frequency