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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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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. These 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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. These differences introduce distribution shifts that can degrade model performance and reliability over time, particularly in “one-to-many” supervision settings where a single human operator oversees multiple agents
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
to build a distributed collaborative file system where control over the data is given to users who can share it directly only with the users they trust and without having to store it at a central authority
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manipulation processes, including realistic post-processing and distribution shifts. Investigate uncertainty, calibration, and reliability measures that can support responsible human decision-making. Develop
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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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are continuously subject to "domain shifts" caused by fluctuating conditions, hardware degradation, or changing physical surroundings. Traditional AI models are often brittle under these distribution shifts, leading
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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