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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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explainable AI for large-scale and complex datasets by developing algorithms, pipelines, and tools suitable for critical decision-making contexts. The doctoral student will be based at the Health Technology
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The increasing prevalence of autonomous systems in dynamic, human-centred environments, such as smart transportation networks and distributed IoT infrastructures, demands decision-making frameworks
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), such as solar photovoltaics (PV), electric vehicles, heat pumps, and storage systems, into distribution networks. Delivering this transition requires coordinated innovation across both active distribution
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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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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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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
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role involves designing and integrating real-time software algorithms with robotic hardware, including perception, control, communication, and safety modules to enable safe, precise, and reliable remote
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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tomography and local adaptive reconstruction to overcome these challenges. Your tasks develop physics-informed, self-supervised learning approaches for phase retrieval implement reconstruction algorithms