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: The ideal candidate should have: * Knowledge of machine learning, especially neural networks or graph neural network or federated learning. * Strong mathematical and algorithmic background (optimization
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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) (required) - Quantum libraries (Cirq, Qiskit, Tensorflow Quantum. QuTiP) (recommended) Theory: - Machine Learning, Deep Learning, Reinforcement Learning, Data Science (required) - Quantum Computing, Quantum
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at Inria Bordeaux has several open PhD positions in human-computer interaction and information visualization. The first round of interviews will take place April 20–24; if you're interested, please contact
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
leverage machine learning techniques to bypass IO bottlenecks in the context of physics simulation on high-performance computing (HPC) clusters. This work is thus placed in a broader ``Machine Learning for
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(EMG), to capture detailed motion, interaction forces, and muscle activity. Predictive Physiological Modeling: Development of machine learning models capable of anticipating motion intent while
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: Modern machine learning approaches are increasingly exploited to automate and optimize fault detection and classification. We propose to investigate methods that improve diagnostics under under-represented
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of: • machine learning • cybersecurity • distributed systems • privacy-enhancing technologies The research will be carried out within the (team name) at LS2N, focusing on trustworthy AI and cybersecurity
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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SUBATECH. The proposed thesis is part of the research activities of the team RAMBO of the RAMBO team (Robot interaction, Ambient systems, Machine learning, Behaviour, Optimization) of the Lab-STICC