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when the shared representations between tasks are limited or trained. This project aims to test these predictions using a behavioral, neural and real-life approach. We will focus on young adults, but
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learning-powered algorithms as well as hybrid approaches, combining either reinforcement learning or deep learning (Graph Neural Networks) with human-based modelling, for fully flawless and autonomous method
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communications Quantum communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
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Engineering, or a related field; a strong interest in machine learning for health, environment or public policy; an experience with Deep learning, Time-series or spatio-temporal modelling, Graph neural networks