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models: deep learning (4D CNNs, Transformers, RNNs) on fMRI and ECoG data Explanatory AI: Applying machine learning models to guide post-stroke neurorehabilitation Predictive AI: Optimizing deep
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metazoans by combining deep learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability
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reflects situated positions. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments). Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant
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d’Aidge (framework de deep learning développé au LIAE), leurs évaluations et leurs validations sur cible. Quelques références: - Cross-Architecture KD: https://openaccess.thecvf.com/content/ACCV2022
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Information Additional comments Candidate Profile and Required Skills: Background in AI: Strong academic background or training in artificial intelligence and machine learning / deep learning. Technical
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learning, particularly with deep learning, perception models, and generative AI. • High proficiency in Python alongside experience using a deep learning framework, with a strong preference for PyTorch
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with deep learning frameworks such as PyTorch, TensorFlow, or scikit-learn Experience with stochastic Machine Learning and uncertainty quantification Experience with multimodal and/or multitemporal
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the
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for High-Performance Automotive ADAS & Autonomous Driving, Product Folder. [7] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," IEEE Conference on Computer Vision
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, Artificial Intelligence, Geosciences, Physics, or a related field Strong programming skills in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or scikit-learn