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of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental studies
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objective of the project is to develop knowledge, models, and algorithms for physics‑informed autonomous control of heavy machinery in uneven and deformable terrain. Specific project tasks include fundamental
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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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plan (e.g., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and
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., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and modeling efforts
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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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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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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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knowledge and fosters the development of highly skilled researchers and professionals. Our research focuses on material properties and manufacturing processes for mainly metallic components, specifically cast
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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