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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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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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demonstrated by publications in international venues in machine learning, AI for science, graph learning or related areas Solid expertise in deep learning, with experience in at least one of the following
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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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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks
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and obsessive-compulsive disorders), and to optimise neuromodulation therapies such as deep brain stimulation. The team combines intracranial recordings and EEG, brain imaging, brain stimulation
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environments (Gazebo, Unreal Engine, or Unity). You have experience in artificial intelligence (Deep Learning, PyTorch) or embedded systems (ROS2, FPGA/VHDL design). You are curious, show scientific rigor and
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, etc.). Robust AI (knowledge of methods for quantifying uncertainty in deep learning or formal verification methods applied to deep learning) Embedded AI Reinforcement learning, supervised and
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detected at a regional scale. The implementation of advanced InSAR processing chains will provide new insights into the phenomena observed and enrich the databases required for deep learning methods
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signal-to noise Post-processing: denoising, reconstruction algorithms Comparison with high-field MRI: deep-learning and other AI modalities for low-field MRI optimization Close cooperation with