190 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"BioData" 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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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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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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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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, data-driven frameworks that enable robust monitoring and condition assessment of infrastructure fleets. By combining smart sensing with distributed intelligence and advanced stochastic modelling
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, and certificates Names and contact details of two referees Further information about the research group can be found on our website . Questions regarding the position should be directed to Prof. Dr
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100%, Zurich, fixed-term We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer interaction. The project will explore how learning systems can
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tracking of minimally invasive robotic systems Autonomous control in uncertain anatomical environments Computer vision for image-guided robotic procedures (incl. endoscopic, MR-, US-guided) Surgical training