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velocity changes at selected locations with the introduction of unsupervised machine learning and study the interaction of mass balance changes (crustal stress changes) and geohazards such as rain-induced
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at international conferences and workshops. Contribute to lab activities and brainstorming sessions. Stay updated with advancements in HCI, machine learning, and sensor systems. Profile We are seeking a motivated
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of technologically relevant oxide thin films. In particular, the combination of state of the art non-linear optics monitoring and electron spectroscopy in situ allows investigating the dynamics
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or similar tools) Initial experience with machine learning, clustering methods or generative AI (preferred but not required) Willingness and ability to collaborate with researchers from different backgrounds
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. Corman and the Institute for Transport Planning and Systems. This project investigates the mathematical, physical, technical, and organizational conditions necessary to enable higher levels of automation
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to gain a better understanding of how cloud microphysical processes drive both precipitation formation and the cloud's radiative impact. Project background The BeyondCloudlab project leverages weather
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microscopy. Workplace Workplace We offer ETH Zurich is a family-friendly employer with excellent working conditions and offers highly competitive salaries . Moreover, our research group offers: An open and
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and original surveys with causal inference and machine learning methods to bring new evidence to bear on the urgent problems policymakers and practitioners face. We collaborate with a nonprofit
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will work within the Computational Mechanics Group of ETH Zürich in collaboration with industrial partners. You will have the unique opportunity to learn, develop and apply a range of cutting-edge
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healthy work-life balance, and as such, we offer modern employment conditions and on-site infrastructure to support you in optimizing your schedule to accommodate your personal interests and family