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capable of breakthroughs. The research will mix state of the art numerical skills with analytic understanding. Your task is to predict new classes of materials that have not been considered before, beyond
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theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive dynamics, and population genetics. This position is part of the interdisciplinary consortium
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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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applicants are expected to have a strong experience in hydrodynamic simulations and radiation transfer, preferably with interest in applying novel computational techniques, in order to optimize
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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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) 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