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interfaces (HMI), and industrial-grade communication protocols for automation in electric power systems.; • Develop and adapt a test network — a simulation model or a replica of a real network — for DIgSILENT
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industrial-grade communication protocols for automation in electric power systems.; Implement the interface between OPAL-RT and HMI/SCADA software: connecting the real-time model or digital twin with the SCADA
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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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Education Institutions. Preference factors: - Knowledge of fundamental concepts related to energy management and gas networks; - Knowledge of optimization and forecasting models; - Knowledge of Python
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designs and results 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: -Modelling electricity markets ; -Modeling energy resources planning ; -Integration of resources for self-consumption
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: - prepare the requirements specification for a software module that allows the use of pre-trained large language models (Large Language Model); - containerization and availability of trained models
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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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of this project is to create a radiomics and radiogenomics based approach to describe and create predictive models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK
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models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning