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. Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, J. Chen, A deep learning-enhanced framework for multiphysics joint inversion, Geophysics, 88(1), K13-K26, 2023. https://doi.org/10.1190/geo2021-0589.1 [3
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include the development of finite elements methods, as well as inverse design strategies based on deep-learning and Neural Networks approaches. The latter will then bring the project to the experimental
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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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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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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requires a deep understanding of the solvents involved, such as highly concentrated aqueous or non-aqueous electrolytes. Accurate modeling of these systems relies specifically on the knowledge
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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. Recent advances in Deep Learning [LeCun2005] make it possible to study approaches based on neural networks to solve complex problems. These networks are resource intensive, often making them difficult
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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep