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Inria, the French national research institute for the digital sciences | Lyon, Rhone Alpes | France | about 21 hours ago
the tactic language and structure declaration mechanism of Rocq more flexible, easier to explore, but also more stable. The design and implementation of new algorithms in type theory might be required
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to process the quantity of experiments conducted with one or more fish swimming simultaneously, on the one hand; and on the other hand, to implement a data fusion algorithm to improve the overall precision
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applications of candidates from a wide range of backgrounds from theoretical physics to pure mathematics. Candidates with experience in tensor network algorithms, rigorous statistical mechanics, probabilistic
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algorithms where the agent can propose updates to its own world model structure, but these updates are only accepted after a formal verification step confirms that the new model still adheres to its core
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applications of candidates from a wide range of backgrounds from theoretical physics to pure mathematics. Candidates with experience in tensor network algorithms, rigorous statistical mechanics, probabilistic
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, particularly in deep learning or related areas. No prior knowledge of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications should include a
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optical medical devices. The works is aiming to develop optical devices and processing algorithms to guide neurosurgeons during the intraoperative neurofunctional assessment and connect these optical
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in the group optical medical devices. The works is aiming to develop optical devices and processing algorithms to guide surgeons during cancer surgery using fluorescence measurements. The post is based
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variational models and deep learning techniques. You will implement and validate reconstruction algorithms, ensuring their performance, robustness, and efficiency for clinical application. You will participate
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop