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modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both engineering and natural systems and develop predictive tools
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compromise grid reliability and energy security. We proceed in two steps. First, we use AI-based document analysis and engineering data to construct detailed bills of materials (BOMs) for selected critical
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examines the cellular and molecular mechanisms that link such conditions to diseases in exposed individuals and their descendants. The lab uses mouse and cellular models and a variety of experimental
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, including data management and the maintenance of computational infrastructure and software. Project background The position is part of the Institute of Microbiology at ETH Zurich and closely linked
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frames). Project background The work focuses on data-driven generation of structural systems. You will be involved in developing, experimenting with, and evaluating machine learning models that help
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COMPAS XR framework developed at ETH Zürich. Project background The successful candidate will work at the intersection of computational design, XR, human-computer interaction, and robotic fabrication, with
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Research focus: Molecular genetics in Arabidopsis to dissect key signaling components linking immune perception to metabolic regulation Phosphoproteomics and targeted metabolite analysis to identify
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
that link molecular design to macroscopic performance in porous polymer aerogels The project is fundamental in nature but is embedded in an industry-sponsored framework that provides clear relevance