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sensing of materials and tissues in a mm-sized chip, which can be manufactured in volume and integrated in industrial and consumer applications, including wearables. Besides developing the sensor, we will
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Sr gases for quantum sensing, the study of many-body physics and for quantum computing. We explore new ways to build sensors, such as superradiant clocks and collaborate with industry, startups and
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, you will: Contribute to defining metrics and indicators across spatial (schoolyard, street, neighborhood, city) and temporal (daily, weekly, seasonal) scales, combining sensor-based data with human
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, seasonal) scales, combining sensor-based data with human-centered behavioral mapping and on-site observation. Help develop harmonized data collection, integration, and analysis protocols ensuring consistency
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the sensors measured? In the ASPIRE project, we develop knowledge for the new generation of sports tracking technology that integrates athletes’ subjective experiences (such as perceived exertion, motivation
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universities, with access to unique deposition and test facilities and strong international collaboration networks. This position provides the opportunity to contribute directly to the energy transition and to
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Trustworthy Graph Machine Learning for Population Scale Networks Job description We invite applications for a postdoctoral researcher to work on fundamental techniques for trustworthy graph machine
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an inspiring research setting at one of Europe’s leading technical universities, with access to unique deposition and test facilities and strong international collaboration networks. This position provides
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the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting
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rate, and wearable sensor data. Develop and validate comfort prediction models targeting high prediction accuracy using a dynamic driving simulator and real-world operating conditions. Integrate thermal