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, including physics-informed neural networks, neural operators, hybrid physics-ML approaches, and emerging foundation-model paradigms for scientific data. Scientific machine learning is increasingly important
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doctorate, contains: Artificial Neural Networks and Large Language Models offer state-of-the-art performance at numerous AI tasks, but being black boxes, they lack explainability which makes them difficult
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retrieve shapes, overlay errors, and other geometrical parameters of the target using methods ranging from local and global optimizers to priors and neural networks developed by partners in the project. Job
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
fitted with sensors, we can also access precise physical measurements. Recent work in AI-for-Science has shown that neural-network-based meta-models can also assimilate measurements. Machine learning
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and cooperation. You have a strong interest in machine learning/artificial intelligence. You have a strong interest in graph-based learning (e.g., graph neural networks). You have experience with deep
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thesis in the field of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously
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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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Technologies (Ireland); and a network-wide training program. Responsibilities and qualifications The PhD scholarship is on the topic of “Carbon-Aware Neural Architecture Search (NAS)”. Most AI models are built
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and benchmarking modern neural network architectures, quantifying the uncertainty of model predictions, and validating your models in our ultrasonic laboratory to bridge the simulation-to-experiment gap
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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