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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023
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, particularly Graph Neural Networks (GNNs), which show great promise. These methods have already demonstrated performance at least comparable to current Track Finding algorithms, with significant room for further
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neural stem cell (NSC) niche. Recent studies have revealed key aspects of human rhombic lip (hRL) organization and shown that defects in its development underlie several human syndromes. These findings
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-to-confidence set conversion' framework recently proposed by the PI (Principal Investigator). The models of interest include deep neural networks with multiple layers and non-linear activations, extending recent
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of compensating for nonlinear PA characteristics under dynamic operating conditions. Advanced machine learning and neural network approaches will be explored to improve linearization performance while reducing
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the exploration of techniques based on physics-informed neural networks and transfer learning. The PhD candidates will have the opportunity to be affiliated with the program BRU21 , with the IoT@NTNU, and with
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interpretable alternative in modelling to the black-box approach of neural networks. Job description The selected candidate will work on the furhter development of spectral submanifold (SSM) methods, aiming
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities
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Description KU Leuven seeks a highly motivated PhD candidate for a fully funded, international doctoral position within the Marie Sklodowska-Curie Doctoral Networks (MSCA-DN) project MicroMan4Health (Website
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neural networks) to design and optimise integrated photonic devices and metasurfaces. This includes building automated workflows that link electromagnetic simulation tools with AI models to accelerate