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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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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers
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imaging and machine learning. The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria
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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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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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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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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