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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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staff position within a Research Infrastructure? No Offer Description This research project aims to develop a synthetic dataset generation technique to optimize the training of neural networks (NNs
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the rapid propagation of pollutants (pesticides, metals, nitrates) toward downstream ecosystems and populations. Assessing these complex accidental chains currently faces three major scientific bottlenecks
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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 8 days ago
expressivity and complexity of neural networks and neural operators, as well as on the development of novel algorithms connecting theory with practice. For more information contact Dr. Ahmed Abdeljawad
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pooling coordination schemes for CF-based 6G RANs. His/Her work will adress objectives like: (i) develop low-complexity ML algorithms for efficient multi-objective network optimization, radio resources
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Overview Are you an expert at finding the "unmet need"? The recently launched Women’s HealthTech Innovation Network (WHIN) is looking for a Strategy & Network Manager to help us develop our strategy
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complex energy and communication networks, requiring a large number of connections and wired conductors. The deployment of these new advanced driver assistance technologies (ADAS) requires the ability
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
capable of making commitments of $5M or more. This position partners closely with senior advancement leadership, development officers, deans, and faculty to manage complex, high-impact fundraising
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signaling cascades that are integrated through dynamic and context-specific networks driving the cellular responses. Given the complexity and dynamic state of signaling networks, the current understanding
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vise à capturer et à échantillonner des distributions de données complexes et de grande dimension, jouant ainsi un rôle central en apprentissage automatique, en inférence bayésienne et en physique