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Field
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. The research will focus on identifying and characterizing ultrasonic signatures emitted by aging electronic components, and on developing physics-informed neural networks (PINNs) to model their degradation
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large-scale spiking neural networks. In close collaboration with our Mod4Comp partners (DFG Forschergruppe FOR 5880), you will develop models of performance and energy to guide the co-design of software
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for quantized and pruned neural networks, creation of quantized and pruned demonstration models, reproduction of state of the art, experiments in heterogeneous quantization Depending on expertise
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different methods of analysis used in the community, in particular linguistic probes (classifiers trained to predict certain linguistic properties from representations discovered by neural networks
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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communications Quantum communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL
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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
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algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong electronics background, including experience in
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of variable distributions [13,14]. Graphic neural networks (GNNs) are new inference methods developed in recent years and are attracting increasing attention due to their efficiency and ability in solving
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real-time? This project will use computational models of neural networks to derive closed-loop control algorithms to modulate oscillatory dynamics in brain circuits. You will test these algorithms