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-informed deep learning is rapidly advancing, integrating artificial intelligence with the governing physical laws to achieve more faithful representations of atmospheric processes. In the field of remote
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influence helps detect labeling errors or prioritize unlabeled images, optimizing the learning algorithm and service quality. The doctoral student will carry out their work at IMAG (UMR of Mathematics) and
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for automatic process generation. Generative approaches, using deep learning algorithms, can generate new process structures, surpassing conventional optimization techniques. Objectives of the ATHENA project
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 16 hours ago
Python and good analytical skills. A good background in probability/statistics and deep learning is expected. Knowledge of differential privacy and/or fairness is a plus, but not necessary. The candidate
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 15 days ago
. [8] Michaela Blott, Thomas B Preußer, Nicholas J Fraser, Giulio Gambardella, Kenneth O’brien, Yaman Umuroglu, Miriam Leeser, and Kees Vissers. FINN-R: An end-to-end deep-learning framework for fast
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Artificial Intelligence (applied mathematics, computer science, etc.), or a thesis defense scheduled for 2025. • Research contributions in deep learning, statistical learning, natural language processing (NLP