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Field
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is part of the PEPR-DIADEM GREENTEA project 'High-speed generation through machine learning of new thermoelectric sulphide alloys composed of abundant elements'. The generation of electricity from
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growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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of the successful candidate. (1) Develop multisource, frugal downscaling approaches. Most downscaling approaches presented in the scientific literature are Machine Learning (ML)-based. The proposing team's experience
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expériences sur les systèmes agroforestiers tempérés ou tropicaux - Maitrise du code ( R , Python) et des méthodes d'apprentissage machine supervisées - Bonnes capacités rédactionnelles et de communication à
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hydrology, hydraulics, environmental sciences or signal processing. A strong expertise in scientific programming (Python, MATLAB) is required. Knowledge of machine learning or deep learning will be a
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operational constraints and employee preferences, within rigorous optimization frameworks. Data science and machine learning: experience with data preparation, feature extraction, and preference learning
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly
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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent
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, artificial intelligence—and particularly machine learning methods—has become indispensable. Depending on the specialty, processed data may include numerical values, point clouds, text or images, often