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learning. Work carried out during the Master's internship has already identified strong trends and tested statistical and machine learning approaches. The thesis will aim to consolidate and update
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simulations, optimisation, machine learning and turbulence modeling. The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning. Website for additional job details https
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behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning levels. The targeted
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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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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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Paris PSL Geosciences Center in Fontainebleau) as well as from the proximity to students working on related topics (e.g., machine learning and experimentation using micromodels). The advances enabled by
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knowledge of multi-objective problems. Master students or Engineers in the field of Process Systems Engineering are strongly encouraged to apply. Knowledge of machine learning algorithms, energy markets and
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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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] 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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being responsible for the confocal microscope (maintenance). Resources provided (equipment, IT, etc.): office equipped with a computer workstation, equipped laboratory bench, reagents and equipment shared