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-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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background in mathematical optimization and development of algorithms would be considered an advantage. You are experienced in conducting independent research and highly motivated to develop mathematical
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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, perform simulation studies, and apply developed methods to empirical datasets. The positions do not involve any lab work. The work includes mathematical modeling, algorithm development, statistical analysis
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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. The role involves contributing to this research project with a focus on model development, implementation, and testing. Further tasks involve dataset curation, analyzing results, and the creation
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Description of work You will be working in the laboratory of Marta Bally (https://ballylab.com
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systems. However, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification. Typical identification algorithms
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we