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the approximation over time, (2) Energy-based optimization that balances accuracy and mesh complexity, or (3) Graph-based techniques that seek near-optimal connectivity structures for mesh representation. • Recent
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of the domain, while the pre-processing step of geometry manipulation and mesh generation is one of the most important efficiency bottlenecks in such methods. The challenge is more prominent in modern, real-world
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The Dynamics of Biomolecular Networks (DYNABIO ) cluster of excellence at the Université Côte d’Azur (Nice, France) brings together 85 research teams from six local biology institutes: C3M (Centre
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experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown
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. CPD has a wide range of applications across domains that require online insights and adaptive decision-making, such as medical monitoring, real-time trading, and network security. A growing number of
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such as principal component analysis (PCA) [2] as well as new types of attacks like link stealing attacks [3] whereby the protected information is not just a dataset but has more complex structure (such as
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a breakthrough concept to upgrade existing fiber optic networks to acoustic sensor arrays, becoming a key component for managing smart cities. Except for a few applications, DAS data are typically
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detection, to cite a few. As telecom fibers are ubiquitous in urban environments, DAS appears as a breakthrough concept to upgrade existing fiber optic networks to acoustic sensor arrays, and a key component
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disability. Surgical management of these fractures is complex, often due to their comminuted nature, intra-articular involvement, and proximity to critical anatomical structures. Accurate preoperative planning
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]. The complexity of these issues is amplified in the FL setting, where each participant has only access to its own data. Candidate profile: The candidate should have a solid mathematical background (in particular