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simulation of algorithms for detecting intermittent faults in compensated networks. Identification and testing of conditions for selective protection of intermittent faults in meshed and radial networks
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achieved by integrating translation mechanisms directly into programmable hardware, including programmable ASICs, to ensure seamless interoperability across diverse network architectures. The goal
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exploring binarized neural networks as potential candidates for privacy-preserving methods in sensitive speech classification tasks. The candidate will begin by reviewing the current state-of-the-art
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of the disciplines of the energy conversion and networks group of the Master (25%) Experience in simulation with MATLAB/Simulink (25%) Experience in implementing laboratory setups (25%) Experience in using the OPAL-RT
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speech processing and speech classification; Research experience with deep neural networks and complex networks; Hands-on experience with Automatic Speech Recognition systems; Hands-on experience with
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experience in Java and Python environments Knowledge of computer networks. Knowledge of information systems and databases Knowledge of with Blockchain development tools, specifically Hyperledger tools General
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by Recovery and Resilience Plan (RRP) https://recuperarportugal.gov.pt/ and Next Generation EU European Funds, is available under the following conditions: OBJECTIVES | FUNCTIONS Deep neural network