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. This postdoc position is part of a national collaborative project involving two major industrial partners — leading suppliers of battery materials and manufacturing equipment for gigafactories. The project is
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for removing atmospheric CO2 and converting it into useful and valuable materials. In this context, our Technology and Society Laboratory (TSL) is looking for a highly motivated PostDoc or Scientist to address
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. Your tasks The postdoc or scientist will model the mass flows of biobased and biodegradable polymers through our society and their releases into the environment. A special focus will be on agricultural
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain. In our Laboratory for Advanced Materials...
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tasks Conduct combinatorial physical vapor deposition and automated characterization together with other team members Perform accelerated optical degradation tests of transparent conductive materials
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain. Dual-comb spectroscopy (DCS) is a rapidly...
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. Empa is a research institution of the ETH Domain. In our research group, we design smart stimuli-responsive (nano)-materials that can be applied as diagnostic tools or as controlled drug delivery systems
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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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-built QCLAS spectrometer for 13CO2/12CO2, both coupled to an automated preconcentration device to separate and purify CO2 from air. As a partner of two European research projects, we are looking for a
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. The acquired and already existing database will be used to further develop ML models for the automated detection of clinically relevant markers. The goal is to develop the technique towards potential clinical