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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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I-2503 – PHD IN EXPLAINABLE AI FOR DATA-DRIVEN PHYSIOLOGICAL AND BEHAVIORAL MODELLING OF CAR DRIVERS
Master’s degree or Engineer diploma in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related field. Experience and skills · Strong knowledge of AI, Machine Learning
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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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accurate computer simulations to predict regimes in which “e-bubbles” display spontaneous Brownian motion, and we have also predicted strategies to create e-bubble currents. It is thus clear
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systems based on an analysis of their current architecture and operational data. Machine learning and neural network architectures, including convolutional, recurrent and transformer networks. MLOps and