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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key
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intelligence called hybrid AI, integrating data driven learning techniques and symbolic or mathematical models that permit us to express constraints and to carry out logical reasoning. ANITI also has ambitious
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your arrival. The EM2C laboratory is seeking a highly motivated candidate for a PhD in data-driven, physics-informed, and probabilistic modeling of turbulent combustion. The PhD work will combine
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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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, without the use of simulation software familiar to process engineers. In this thesis, we aim to: - Propose generative models for other types of cycles, based on existing models. To do this, we could use
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particle detectors, as well as technical research and development and associated applications for energy, health, and environment. The laboratory has important technical staff (approximately 280 engineers