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] Cross, E. J., Gibson, S. J., Jones, M. R., Pitchforth, D. J., Zhang, S., & Rogers, T. J. (2021). Physics-informed machine learning for structural health monitoring. Structural health monitoring based
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will build on recent advances in machine learning for dynamical systems to extract meaningful representations of complex flame dynamics, construct prognostic ROMs, and perform data assimilation
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advanced seismic methods (including array processing, machine learning, and potentially distributed acoustic sensing) to develop novel approaches for monitoring unsteady and non-uniform flood flows across
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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of machine learning algorithms are of real interest in improving the accuracy of water quality measurements, particularly in identifying, accounting for, and neutralizing ionic interference. The second key
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psychological theory, cultural history, and interdisciplinary research. Good academic writing and communication skills in English. Desirable skills Knowledge of machine learning or advanced NLP techniques (e.g
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, Communication, Optimization • SyRI: Robotic Systems in Interaction The PhD student will join the CID team, whose research focuses on Artificial Intelligence, including statistical learning, uncertainty management
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and image processing. Prior experience with data fusion, machine learning, or super-resolution methods will be considered an asset. Candidates should be motivated to conduct independent scientific
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Microelectronics teams, the PhD student will be supervised and helped. He/She will access, after training, the IEMN technological platforms. He/She will be provided the tools and computer accesses necessary
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into **influence functions**, theoretical tools designed to quantify the impact of a sample on a machine learning model. These functions, defined through the derivative of model parameters or the loss function with