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Field
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, which options become visible, and how agency is distributed between humans and algorithmic systems. In this PhD project, you will study AI-supported decision-making from a representational perspective
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estimation methods for deep neural networks. A principled Bayesian framework for multimodal uncertainty modeling. Robust learning algorithms under missing modalities and distribution shifts. New uncertainty
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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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estimation in dynamic. Building upon this RF-based perception layer, the project will develop distributed coordination and online trajectory generation strategies, allowing the swarm to adapt its behavior in
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, Intelligent_Mapping is integrated into the IRIMA Plateformes Consortium, supported by BRGM (PI: J. Langlois). The primary aim of Intelligent_Mapping is to develop Artificial Intelligence (AI) algorithms able
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of simulated sensors (vision, sonar, LiDAR, inertial navigation). - Implementation of guidance, control, and planning algorithms under environmental constraints. - Implementation of distributed simulation
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
Science'' context, which aims at using ML to solve key problems arising in traditional sciences. More specifically, we will focus on distributed dimensionality reduction techniques, which critically reduce
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foundations in waveform design and advanced signal processing. In particular, energy-efficient precoding algorithms have been developed for distributed MIMO systems and are currently validated through numerical
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several related areas of expertise: generative AI for robotic planning and control, cognitive control, distributed perception and control, and HW-SW co-design of algorithms. Our research is applied across
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practice offers no robust, scalable, or provable mechanism to guarantee this right once a model has been trained. The distributed nature of federated settings introduces unique challenges for unlearning