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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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to real-world error patterns by means of learning methods. This requires a recognition of the diverse and complex error patterns that can occur in quantum devices in real-world scenarios. To achieve
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. Experimental characterization of Hall effect thrusters using combination of diagnostic techniques such as optical emission and absorption, Langmuir probes, etc. enhanced by the application of machine learning
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SICAL team at LIRIS, recognised for its expertise in HCI and education, including adaptive gamification, engagement, learning analysis, and the design of motivational affordances in education. They will
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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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professionals and school staff of the territory, in a participatory research approach. The IHES team at EBI collaborates closely with the Centre for Study and Research on Cognition and Learning (CeRCA
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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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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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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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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