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! This unique research opportunity revolves around the physics of distributed robotic systems, mechanical metamaterials, active matter, and embodied intelligence, combining table-top experiments and
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-impact projects across our core focus areas. We are looking for curious minds who are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https
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-impact projects across our core focus areas. We are looking for curious minds who are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https
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of the Software Engineering unit. About the project Software is today prominent in many critical systems and infrastructures, such as power distribution systems, railway infrastructure, or flight control systems
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sources, and manipulation processes, including realistic post-processing and distribution shifts. Investigate uncertainty, calibration, and reliability measures that can support responsible human decision
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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
infrastructures such as IPFS (https://ipfs.io/ ) and Matrix (https://matrix.org/ ) on which we can plug replication mechanisms for file system synchronisation. Data replication algorithms have to be reliable (i.e
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interfaces to share intermediate data for distributed training and processing, generating large traffic flows. Low and deterministic latency will be required for specific application in data centre for AI
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, manipulation, and other adversarial attacks. Conventional cryptographic methods alone may not be sufficient for highly dynamic, distributed, or resource-constrained wireless systems. The project aims to develop
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-world environments inevitably face noisy data, distribution shifts, and situations their training never anticipated. Existing machine learning research focuses on limiting the impact of such disturbances