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investigate the use of graph neural networks to solve graph and combinatorial problems, such as approximating centrality measures or performing network alignment. • Computational Neuroscience. We are interested
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Data Management and Network/Computing ContinuumCloud computing has significantly increased the volume of data consumed daily, but it has also centralized somehow the storage of data. With
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zero net magnetization. The objective is to understand the interplay between magnetic and structural degrees of freedom gives that give rise to this novel phase, and to explore how the properties can be
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which neural network effectively conveys this information are central questions in the field of hearing and neuroscience in general. We are developing several complementary approaches to answer
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Lomet, Research Engineer in AI at LIAD, CEA Saclay · Marianne Clausel (University of Lorraine), scientific lead of the national PEPR causali-t-ai program, · Myriam Tami, Associate Professor
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-edge research in systems and computational biology, with a central focus on the architecture, dynamics, and interactions of biomolecular networks. The interdisciplinary approach of DYNABIO bridges
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! Education Master's degree (Bac+5) in telecommunications, computer science, or a related field, with an interest in AI, cognitive networks, or connected vehicles. Experience and skills Prior experience with
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-edge research in systems and computational biology, with a central focus on the architecture, dynamics, and interactions of biomolecular networks. The interdisciplinary approach of DYNABIO bridges
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this Ph.D. topic proposal: • The optimal approximation of 3D shapes using meshes is known to be a NP-hard problem. This means that finding the best possible mesh representation for a given shape, while
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-edge research in systems and computational biology, with a central focus on the architecture, dynamics, and interactions of biomolecular networks. The interdisciplinary approach of DYNABIO bridges