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services trading in electricity markets; -Self-consumption and impact on wholesale market; -Planning energy resources; -Write reports and papers for international conferences and journals with the new
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resource utilization, leading to performance losses and excessive energy consumption. The problem is exacerbated in distributed environments, where hundreds or thousands of GPUs operate suboptimally
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advantage of its high performance and low latency. Furthermore, these goals are essential for enhancing the use of storage resources in HPC systems and for defining new best-practice guidelines for users.; 3
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workload’s data (e.g., Deep Learning, Large Language Models) while addressing the I/O interference and fairness challenges faced by current distributed infrastructures, where storage resources are being shared
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monitoring frameworks, and on measuring the energy impact incurred over different computational resources. Minimum requirements: - Experience with software-defined control systems (e.g., Cheferd, PAIO, PADLL
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leveraging on AI and optimization, applying data science and analytics techniques. Such tools will support the integration of Distributed Energy Storage (DES) and Distributed Energy Resources (DER) in