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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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between the (relatively few) models of early contrail development which operate at the plume scale, regional- to global-scale models of the cloud response, and real-world observations of both. The lack of a
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and their CI/CD integrations, providing well-structured code and documentation Experience in working with cloud, HPC or cluster compute resources including working in a Linux environment and command
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of telecommunications, AI and cloud computing technologies. Key Responsibilities: Use of ML to handle resilience and fail over in 6G architectures Design and develop robust network slicing in 6G architectures