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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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laboratories at CY Cergy Paris University are contributing to this initiative through their work on quantum physics, quantum error-correcting codes, and quantum computing. The medium- and long-term goal
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computing. This will include, but is not limited to, the design of distributed quantum algorithms, circuits, and error correction, as well as the interplay between circuit optimization and circuit
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located on the Huddinge campus of the Karolinska Institute. The overall aim of our research group is to understand cytotoxic lymphocyte biology, in order to explain and better treat severe inborn errors
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procedures for structural systems with the graduate student Consequence of Error/Judgement No applicable Supervision Received Prof. Tony T.Y. Yang (yang@civil.ubc.ca ) will provide high-level supervision
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built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
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factors: Preference will be given to candidates with: - Experience in fault injection systems - Experience in ML systems EVALUATION CRITERIA The selection will be based on the following criteria: Interview
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influence helps detect labeling errors or prioritize unlabeled images, optimizing the learning algorithm and service quality. The doctoral student will carry out their work at IMAG (UMR of Mathematics) and
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aligning with NQTP Missions 1 and 2 and NQCC Testbed programme, will tailor the developed benchmarking approaches to error-corrected as well as distributed quantum computers, addressing the need for scalable
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fault-tolerance. Due to the critical nature of many distributed systems, their correctness is of crucial importance. The verification of distributed systems, however, is notoriously difficult. This PhD