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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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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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authorities. École des Ponts ParisTech, in accordance with its strategic plan, develops a long-term research activity in the field of Machine Learning and Computer Vision. The IMAGINE team is a renowned
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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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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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: 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