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will combine machine learning (generative models, graph learning), management of heterogeneous and incomplete data, and a strong interdisciplinary dimension with the materials science experts of the
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machine-learning techniques. While recent theoretical work has examined market outcomes under algorithmic pricing, the determinants of firms' adoption and switching of such technologies remain poorly
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background noise (e.g., PMT dark counts, accidental coincidences). • Integrate machine learning techniques (e.g., boosted decision trees) to improve signal identification and background rejection
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ability to develop reproducible processing procedures. * Knowledge of machine learning and an interest in language models. * Ability to analyse complex data and assess the uncertainties and limitations
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biomechanical modeling, ideally applied to rigid multibody systems and to the musculoskeletal system. - Python programming (NumPy, SciPy, scikit-learn); interest in statistics, machine learning, model
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intersection of paleontology, biology, and physical chemistry. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UAR3461-REGOPR-028/Default.aspx Requirements Research Field Biological sciences Education
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simultaneously from both partners. Machine-learning algorithms will subsequently be developed, trained and tested to identify markers of reduced brain resilience during ageing. The project adopts an
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- Data Science - Geometry, Learning, Information and Algorithms - Speech and Cognition The Gipsa-lab comprises 150 permanent staff and approximately 250 non-permanent staff (doctoral students, post
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organized into five departments: Algorithms, Learning, and Computation; Data Science; Fluid Mechanics and Energy; Human-Computer Interaction; and Language Science and Technology. The IDEFIX project is led
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sequencing, and the overall system design by using finite element modelling (FEM) and machine learning (ML) tools. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR22-MARPEC-007/Default.aspx