185 data-"https:"-"https:"-"https:"-"https:"-"https:"-"RMIT-University" positions at ETH Zurich
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of research interests related to the research offered (1–2 pages) Names and contact information for ideally three (or at least two) references The evaluation will start on January 8, 2026, and will
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the most recent draft of your thesis. Please note that applications without these documents will not be considered. More information about our research can be found at the website of Sustainable Food
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animals. Across all three components, the project will involve high-resolution mass-spectrometry and complementary data streams to explore how exhaled metabolites reflect physiological status. Job
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portal. Applications via email or postal services will not be considered. Candidates will be informed if their proposal is accepted for furhter consideration in January 2026. For information about the gta
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transfer, developing and employing laboratory experiments, computer simulations, and field analyses. Our aim is to gain fundamental insights and to develop sustainable technologies that address societal
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core part of your project will be interpreting the data and developing scientific hypotheses about the atmospheric processes that control the cycling of selenium and other trace elements. You will work
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, we collect data through biological monitoring, environmental DNA methods, remote sensing, and field sampling, and use these data to answer questions with statistical and process-based models
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climate-neutral future . Curious? So are we. We look forward to receiving your online application, which should include: A letter of motivation Your CV Contact information for two references
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methods in learning sciences and educational research You are preferably studying Computer Science or a related field You are interested in Learning Sciences or Human-Computer Interaction (HCI) You are
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, data-driven frameworks that enable robust monitoring and condition assessment of infrastructure fleets. By combining smart sensing with distributed intelligence and advanced stochastic modelling