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the mobility of IoT devices. This thesis proposes leveraging intelligent softwarization—using Machine Learning (ML), Software-Defined Networking (SDN), and Network Function Virtualization (NFV
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 19 days ago
collaboration with L. Bonati at IIT Genova, who developed the library mlcolvar, https://github.com/luigibonati/mlcolvar ). 2) Compare the data-science dimensional reduction approaches above, with machine learning
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to the development of state-of-the-art AI approaches applied to land surface monitoring, particularly using satellite observations. These approaches may include machine learning and deep learning methods
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motivated to acquire new skills. Candidates must be fluent in English and/or French with scientific writing skills. The doctoral contract will take place at the CRISMAT laboratory (https://crismat.cnrs.fr
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 1 month ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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problems, statistical learning and machine learning (machine learning, deep learning) - Knowledge of associated software development tools and environments: Python, PyTorch, Scikit-learn, Jax, Julia
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AI for Science and Engineering and Foundation Models and Generative AI axes. The recruited candidate will develop contributions at the interface between machine learning, optimization and quantum
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the French project BIBLISSIMA+ (https://projet.biblissima.fr ), and the ERC Synergy MIDRASH (https://www.midrash.eu ). The eScriptorium project itself involves North American and European teams and aims
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analysis and processing: NumPy, Pandas, SciPy; - Machine learning/AI: Scikit-learn, TensorFlow, PyTorch (preferred); - Data visualization: Matplotlib, Seaborn, Plotly. LanguagesFRENCHLevelGood
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preparatory cycle), the engineering cycle as a student and/or apprentice, and the Master’s degree programme (M1 and M2). He/she will primarily contribute to teaching computer architecture and microprocessors