20 design-"https:"-"https:"-"https:"-"Institute-of-Computer-Science" positions at Empa
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scalability and material availability. A knowledge of the asphalt mechanical perfor-mance and pavement design concepts will be considered an advantage. We are looking for a highly motivated team player who is
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carbide ceramics. This project is a close collaboration between the Smart Materials Processing and Architectured Materials groups of the laboratory and focuses on the design, fabrication and
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. Empa is a research institution of the ETH Domain. Empa’s Laboratory Materials for Energy Conversion focuses on materials and device innovation for sustainable energy conversion and storage technologies
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
systems Designing and synthesizing new monomers, modified backbones, or grafted polymer architectures to deliberately tailor hierarchical pore structures Establishing clear structure–property relationships
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. Empa is a research institution of the ETH Domain. Empa’s Laboratory Materials for Energy Conversion focuses on materials and device innovation for sustainable energy technologies. Your tasks This PhD
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will contribute to the NCCR Separations . Your tasks Development of an advanced testing infrastructure for sorbents and processes in DAC: Planning, design and implementation of novel infrastructure
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the growing challenge of infections caused by resistant bacteria through designing innovative, non-toxic, and durable antimicrobial solutions. This highly interdisciplinary project will be conducted in
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
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solution for through-thickness reinforcement of FRPs. Your tasks You will work in collaboration with a postdoctoral researcher/scientist mainly on: Design and manufacturing of SMA Z-pinned FRPs SMA
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness