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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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the algorithmic capabilities of intelligent systems, reducing their computational costs, and bridging the gap between hardware and algorithm design. Duties and responsibilities The appointment as a postdoctoral
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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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to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and innovation. We believe that knowledge and new
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Sapere Aude – dare to know – is our motto. Our students and employees develop important knowledge that enrich both the individual and the community. Our academic environment is characterised by
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Computer Vision algorithms. Experience using urban building stock modelling and urban digital twins What you will do: Design & Develop: Create data structures for detailed, spatialised construction component
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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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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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to achieve scalability in terms of the simulator systems. The work will be done in close collaboration with the physics team to be able to develop optimizations also at the algorithmic level in a co-design way
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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