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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 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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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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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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, 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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will work with deep learning, affective computing, multimodal signal processing, graph neural networks, hypernetworks, temporal modelling and responsible AI. Expected outputs include personalised
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. The position in Heilbronn is already filled. This job description is for a position in Garching . Here, you will develop general-purpose, scalable linear solvers for training neural networks that represent
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selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing
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the neural mechanisms underlying cognitive development and learning. Our work thus bridges fundamental neuroscience with questions of direct relevance for education and child development. To address
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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