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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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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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this goal, it is paramount to characterize the added value of using machine learning in estimating and decoding quantum errors occurring in coded quantum systems. Research program: The PhD student will first
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: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database
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. The project proposes an innovative approach to model sea ice dynamics from the ice floe scale to the basin scale, leveraging hybrid data assimilation and machine learning methods to shape a physically robust
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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
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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between
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transfer learning to transpose the recurrent neural network (RNN) model available for supercritical CO2 power cycles to other cycles. Since thermodynamic conditions vary greatly depending on the fluid and
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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large