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, including deep learning architectures, self-supervised and unsupervised approaches, physics-informed neural networks, transformer-based models, and/or quantum-inspired learning techniques, capable
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Engineering, Communication Engineering, Computer/Data Science, Mathematics or equivalents; – background in artificial intelligence, image/signal processing, remote sensing, passive/active sensors. Where
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Symbolic AI, Neuro-Symbolic AI, Agentic AI, Neural Networks for code vulnerability detection (Senanayake et al. 2024), SBOM tools, prompt vulnerability detectors, and static/dynamic analysis tools could
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collaborative IoT scenarios; ii) strategies for efficient and adaptive learning on-device or across a network of heterogeneous nodes while minimizing energy consumption and bandwidth usage; iii) investigating how
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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within a Research Infrastructure? No Offer Description Neural networks are known to be universal approximants for any function in an arbitrary number of variables. This property has been exploited in
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precision. By harnessing quantum phenomena such as superposition and entanglement, quantum sensors can detect minute variations in observables such as magnetic fields, electric currents, temperature
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Energy Storage Technologies RESTORATIVE is a pioneering Marie Skłodowska-Curie Actions (MSCA) Doctoral Network dedicated to accelerating the green transition through Thermo-Mechanical Grid-Scale Energy
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Description CanGoNano is a European Training Network funded in the framework of HORIZON Europe Marie Skłodowska-Curie Doctoral Networks (MSCA-DN), focusing on glyconanomaterials (GNM) as precision tools
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. More specifically, the doctoral program has four different specialization tracks: - Complex Systems and Networks (CN) - Computational Mechanics (CM) - Learning and Control (LC) - Software Quality (SQ