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++, Python, Rust, …). Demonstrated experience in one of the following areas, with a willingness to learn one another: (1) genome sequences and omics data, (2) deep learning, and (3) compressed data structures
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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fundamental and applied contexts. Using state-of-the-art laboratory facilities, we advance understanding of turbulent incompressible, compressible, and multiphase flows through coordinated research
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techniques such as tensile, compression, DSC/TGA, etc. as well as analysis of the data with good knowledge of possible errors. hands-on experience with alloy design strategies, including high-throughput
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include surfactants, polymers and biomolecules. The current project focusses on Soft Matter, in particular on the study of the flow properties of compressible colloids, microgels, combining scattering
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techniques such as SEM, TEM, XRD, EDS, EBSD, FIB/SEM etc., as well as physico-mechanical characterization techniques such as tensile, compression, DSC/TGA, etc. as well as analysis of the data with good
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at the Division covers turbulent flow (both compressible and incompressible), multiphase flows, aero-acoustics and turbomachines. Our tools include both computations and experiments. The research covers a wide