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Field
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using techniques from complex-network theory. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1055
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Are you ready to dive into the fascinating world of resilient optical transport networks? We're on the lookout for a talented Postdoc to join our dynamic, international team at the Optical Networks
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
, seasonal and solar cycle dependent variabilities. These different phenomena make characterization of regional ionospheric dynamics a complex problem. New observational capabilities and sophisticated data
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interactions between these levels as well. In order to capture the structure and complexity of relationships between the nodes of networks with a mul-tidimensional nature, tensors are used to represent
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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing
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to advance the resiliency and reliability of beyond-5G/6G networks. The candidate is expected to work on the European project PHRESH (https://itea4.org/index.php/project/phresh.html ) and the Dutch National
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and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems, with particular emphasis on smart mobility and urban transportation networks. In particular
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high-value polymers from complex waste streams such as multilayer packaging and mixed plastic waste, leading to large volumes being incinerated or landfilled. Solvent based recycling offers a promising
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. The appointee will work on a project focused on next-generation communication and sensing technologies for enhancing safety and performance in intelligent networked systems. The appointee is expected to make
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 1 month ago
complexes. The successful candidate will develop novel graph neural network (GNN) architectures to learn dynamic information from molecular dynamics (MD) simulations of protein-protein and protein-nucleic