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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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-Service (MaaS) ecosystem. The work will integrate deep reinforcement learning, autonomous agent modelling, and multi-objective optimization to enable predictive simulation, real-time resource management
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