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this goal, it is paramount to characterize the added value of using machine learning in estimating and decoding quantum errors occurring in coded quantum systems. Research program: The PhD student will first
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. Experimental characterization of Hall effect thrusters using combination of diagnostic techniques such as optical emission and absorption, Langmuir probes, etc. enhanced by the application of machine learning
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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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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep
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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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will build on recent advances in machine learning for dynamical systems to extract meaningful representations of complex flame dynamics, construct prognostic ROMs, and perform data assimilation
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simulations, optimisation, machine learning and turbulence modeling. The researcher must hold a Phd in fluid mechanics / Applied mathematic / Machine Learning. Website for additional job details https
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learning. Work carried out during the Master's internship has already identified strong trends and tested statistical and machine learning approaches. The thesis will aim to consolidate and update
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect