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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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operating scenarios for power networks with a high penetration of renewable energy—one of the services to be offered by the collaborative laboratory currently under development.; This research grant offers a
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. The project seeks to transform existing submarine communication infrastructure into a large-scale sensor network for the detection and monitoring of geophysical phenomena. 4. REQUIRED PROFILE: Admission
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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Development of novel Machine Learning techniques applied in systems/networks research, which includes, but is not
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-of-the-art deep neural networks for musical audio, with special focus on timbre analysis and manipulation.; - Identify and implement approaches for explainable ML models.; - Cooperate in writing scientific
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distribution networks, considering solutions such as FL Energy's OperatorFabric.; - Study human-machine interaction methodologies that reduce the complexity of supervising multiple distribution networks
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of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
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of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
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neural networks, enabling us to estimate the reliability of a single decision of this algorithm. Regarding generalisation, recent self-supervised learning paradigms have strong synergies with the multi
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validate the tools identified and/or developed, using as a reference case the remote training of grid operators in operational scenarios for power networks with a high penetration of renewable energy—one