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Doctoral student in development of nanowire devices for photonic neuromorphic computing (PA2026/472)
dynamic photonic sensor array with on-chip neuromorphic intelligence. This is enabled by significantly advancing the state of the art in optoelectronic algorithm-hardware co-design and multi-scale
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chain management and the potential applications of data-driven methodologies and algorithms.• You can do independent research (demonstrated, e.g., by an excellent MSc thesis).• You have excellent command
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development of post-graduate students. Particular attention will be given to candidates with experience in topics that are relevant to data science, most notably mathematical and algorithmic foundation
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fidelity, enabling precise characterization of transmitter errors and nonlinearities. These measurements will be used to generate error signals for advanced AI-assisted digital predistortion algorithms
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associate will participate in the design and implementation of the reference data model to ensure simulation system interoperability. Additionally, they will develop AI algorithms and multi-criteria
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to be essential. Certain conflicting objectives then arise (computation time, optimality of the solution, design and implementation time of the algorithm used, etc.). For the engineering researcher, who
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, retrieval algorithm, applications in hydrology) and will benefit from their knowledge and experience on the mission. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5126-ARNMIA-005
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to the fundamentals and algorithms of spatially and time-multiplexed oscillator-network computing. Duties The PhD student will focus on the fundamentals and algorithms for spatially and time-multiplexed oscillator
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, quantum computing algorithms/architectures, applications of quantum computing; or 2. Quantum photonic integrated circuits. Candidates must hold a Ph.D. in Electrical Engineering, Computer Science, Physics
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. Wallace (1996). MML estimation of the parameters of the spherical Fisher Distribution. In S. Arikawa and A. K. Sharma (eds.), Proc. 7th International Workshop on Algorithmic Learning Theory (ALT'96