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Would you like to put your mathematical and algorithm skills to work in a truly interdisciplinary project and be part of devising new sustainable cement formulations through improved dynamic X-ray
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structure, to maintain a specific lipid and protein composition in cilia. Cilia are evolutionary conserved organelles that protrude from the surface of eukaryotic cells to facilitate motility and sensory
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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machine learning in, for instance, information retrieval, recommender systems or NLP. The research will involve developing new algorithms that are compatible with photonic neural network architectures
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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the project. Disseminate research findings through publications and presentations at international conferences. Expectations of qualifications: PhD in Ecology, Evolutionary Biology, or a related discipline
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes