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systems Reinforcement Learning and Agentic Control: Hands-on experience with reinforcement learning, multi-agent systems, or planning-based agents for autonomous vehicles or robots operating in dynamic
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control) using real operator-grade datasets (traffic indicators, network KPI's, configuration logs and energy measurements when available). • Investigating and combining multiple energy-saving levers (e.g
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uncertainty, robustness, and reproducibility in experimental data Contribute to feature selection and model interpretability, supporting biologically and regulatory-meaningful decision making Collaborate
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