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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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) in computer science, mathematics or statistics, with an excellent publication record. Solid research experience in one or more of the following topics is expected: Graph neural networks Optimization
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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating
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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating
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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating
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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating
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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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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating
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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating
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neither guaranteed (optimism) nor impossible (pessimism). MePhiS aims at (1) reinterpreting ethical and other philosophical issues by emphasizing their entanglement with suffering; (2) integrating