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deadline. ================================================ The description of the research topic "Mathematical foundations of approximation by (deep) neural networks" is as follows. Recently, the
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Leibniz-Institut für Astrophysik Potsdam (AIP) | 2 months ago
modelling. At the core of this newly funded Leibniz ScienceCampus SCALES are next-generation simulation techniques, hybrid modelling strategies, and the use of machine learning, particularly neural networks
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relies on three pillars: Optimal control theory , Machine learning and neural networks, Closed-loop control theory. While optimal control tools enable the description of optimal collision avoidance
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complex, structured data, with a focus on graphs. It will investigate methodological improvements to Graph Neural Networks (or related models), based on Network Science and structural properties of the
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Your Job Power grids are naturally represented as graphs, where buses, lines, transformers, generators, and loads interact through physical constraints. Recent developments in graph neural networks
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and computational aspects of quantum many-body systems, including Tensor Networks, Neural Quantum States, Stabilizer formalism, Complexity measures such as entanglement and quantum magic, quantum
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neural networks connecting prefrontal cortex and subcortical regions in non-human primates. We combine electrophysiology, chemogenetic circuit manipulation and computational modeling to analyze neural
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foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern neural networks, covering
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Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH | Barcelona, Cataluna | Spain | 3 days ago
Technical Skills: Multidimensional and temporal data recording. Training and inference in neural networks (in particular, optimization by generalization). Generalization of networks in different data
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Universitat Politècnica de Catalunya (UPC)- BarcelonaTECH | Barcelona, Cataluna | Spain | 18 days ago
neural networks (GNN). Functions to be developed: Build and openly publish a collection of GNNs trained on tasks with known answers (minimum paths, flows, spanning trees), to validate interpretability