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Field
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(PSI), within the research group EAVISE. The project explores audio representation learning for low-resource settings. Recent advances in machine learning for audio have focused on learning
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computer vision models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek
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structure with assembly and optical properties. Your role will be to tune the molecular structure of the ligands and change ligand shell structure. Using advanced scattering and microscopy, you will study the
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of mixed-oxide catalysts for application in chemical looping processes aimed at energy transition and industrial decarbonization. The work will involve Advanced synthesis of mixed oxides, doped systems, and
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analysis will be used to study the seismic response of prototype structures for different scenarios. Hence, the project integrates advanced element-scale, model-scale and in-situ testing with state
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of structural lightweight construction, energy research and medical technology. The experimental development is closely accompanied by modelling approaches and artificial intelligence methods. Interested? Please
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-scale modelling, machine learning) High resolution analysis, monitoring of chemistry, structure and transformations at the atomic scale of buried interfaces and defects by correlated experimental
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the field of optics. The research group conducts applied research in fields such as remote sensing, communications technology, autonomous platforms, power electronics, energy systems and advanced control
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the formation of double-strand breaks, copy-number alterations, and other structural genome changes. Our group combines cutting-edge genome biology approaches with quantitative, multi-omics analyses to uncover
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on the development of new antenna and integrated circuit concepts for radar and electromagnetic modelling. The Electromagnetics (EM) and Integrated Circuit (IC) groups are involved as well as the Center for Wireless