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. Empa is a research institution of the ETH Domain. The positions will be in the research group Reactors and Processes for Power-to-X, which is part of the Chemical Energy Carriers and Vehicle Systems
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. Empa is a research institution of the ETH Domain. For an applied research project, we are seeking a highly motivated Postdoc interested in the development of a hybrid AM manufacturing process for silicon
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of measurement systems, signal processing and analysis and the assessment of measurement accuracy, robustness and long-term stability. The resulting data form the basis for model-based approaches to evaluating
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, a broad interdisciplinary environment and competitive salaries. The PhD student will be enrolled at ETH Zurich and will be affiliated with the Swiss Institute of Bioinformatics. The position is
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of an ICOS flask autosampler for VOC analyses. Installation, operation, and maintenance of the analytics and metrological measurements. Automation of measurement and calibration routines, data
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for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and institutions, supported through dedicated
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training, exchanges, and career development for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
develops advanced and/or low eco-impact, porous materials for insulation, sorption, and energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property
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field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy