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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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This project targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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Are you excited about pioneering experimental quantum computing? Do you want to be part of a world-class research environment developing the next generation of superconducting quantum processors? We
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Biochemistry advances multiphase flow and separation science to accelerate industrial innovation and implementation. About the research project The project aims to develop hybrid quantum–classical approaches
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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Safe and efficient ice navigation supported by satellite data Join us at the Division of Geoscience and Remote Sensing and help advance knowledge about sea ice dynamics and develop the capability
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Do you want to contribute to groundbreaking research in the development of a theoretical framework and numerical algorithms for evolving stochastic manifolds? This is an exciting opportunity for a