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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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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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. Empa is a research institution of the ETH Domain. To strengthen our team and enhance our knowledge and understanding in pyrolysis processes we are looking for a PhD student for scientific analysis
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property relationships, and application-driven R&D, in close collaboration with academic and
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. Empa is a research institution of the ETH Domain. The Laboratory Chemical Energy Carriers and Vehicle Systems Laboratory conducts, develops and optimizes processes for renewable fuels. The group
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property relationships, and application-driven R&D, in close collaboration with academic and
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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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, antibiotic resistance genes, VOCs and PFAs. Investigations on electrode materials, manufacturing processes, signal amplification and modulation are underway. We strive to strengthen environmental applications
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related 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