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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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, or the misinterpretation of some inputs to result in a particular behaviour that is favourable to the attacker. In this thesis, the PhD candidate will first study the impact of decentralizing ML algorithms
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have a PhD in computer science, mathematics, physics, or related fields, with a passion for programming. A desire to contribute to the development of open-source software within the context of the agreed
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involving edge-to-edge exchanges and mobility.Tasks/Responsibilities- Implement and validate an indexation algorithm based on existing publications from the STACK research team [1, 2]- Design and analyze a
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] and taphonomy of animal bones [Cifuentes-Alcobendas and Dom´ınguez-Rodrigo, 2019] are gradually intensifying. Thus, the present PhD project is an opportunity for the development of original ML solutions
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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization
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procedure. In this context, the proposed PhD project aims to develop an innovative strategy to evaluate the efficiency and quality of surgical care. This strategy is based on data science, combining
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric