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are primarily designed for batch setting and are centralized, making them unsuitable for handling continuous IoT data streams at scale. The objective of this PhD thesis is to investigate the autoML problems and
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. The simulated robot data is easier to construct, but still requires much engineering work to create scripts and suffers from a sim-to-real gap. The real-world Internet data though is large-scale and contains real
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. A third challenge is how to retrain and update the model in a setting where transferring large amounts of data to a central server is prohibitive in terms of time and energy. This research is
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3 Apr 2024 Job Information Organisation/Company Inria Research Field Computer science Researcher Profile Recognised Researcher (R2) Country France Application Deadline 2 May 2024 - 00:00 (UTC) Type
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complexity and large number of parameters. Tensors are a natural way to represent high dimensional data for numerous applications in computational science and data science [1]. CP, Tucker and Tensor Train are
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3 Apr 2024 Job Information Organisation/Company Inria Research Field Computer science Researcher Profile Recognised Researcher (R2) Country France Application Deadline 2 May 2024 - 00:00 (UTC) Type
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cluster computer system to realize big-data analysis and simulations. Mission confiée Context of the project Artificial Intelligence (AI) and especially Deep Learning (DL) have undergone many successes in
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the model and to build a "ground truth" that would be the most plausible. Data used for this PhD will come from existing datasets. Principales activités - Analyse and process EEG and fMRI data - Propose
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designing custom and embedded Artificial Intelligence HardWare architectures (AI-HW) to support energy-intensive data movement, speed of computation, and large memory resources that AI requires to achieve its