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qualifications include strong analytical and quantitative skills in numerical analysis, programming, high-performance computing, as well as skills in dynamics & structural health monitoring, data analysis and
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challenges. The work is conducted at the interface of mechanics, artificial intelligence, and computational science. The developed methods will be validated on benchmark problems and real-world data and
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) Unsupervised machine learning and deep learning methods Analysis, visualization, and interpretation of learned design spaces Contributing to research outputs (prototypes, publications, open-source code) Profile
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-facturing processes. In this internship, you will work on state-of-the-art anomaly detection methods using computer vision and time-series data, with a particular focus on multimodal data fusion for powder
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biomedical processes, particularly in complex or technically challenging settings. To address these questions, the lab develops and applies new methods for metabolomics, lipidomics, and ¹³C metabolic flux
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properties. In parallel, they continuously sense and respond to diverse mechanical cues from their environment, including adhesion, stiffness, tension, shear, pressure, and confinement. These cues
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60%-80%, Zurich, fixed-term Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and data processing? We invite you to join
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atmospheric/environmental sciences, physics, computational science, engineering or a related field A background in numerical modelling, statistical data analysis and/or programming skills is highly encouraged
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processes. The PhD student will become an expert in numerous fabrication techniques such as electron beam lithography, physical and chemical etching, deposition and electron microscopy. In addition
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100%, Zurich, fixed-term The Computational Design Lab is an interdisciplinary research group at ETH Zurich, led by Prof. Dr. Bernd Bickel . We develop novel algorithms and next-generation