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techniques in VLBI. The primary objective is to develop and apply novel strategies based on AI/machine learning (ML) to enhance VLBI data analysis and simulation. Specific tasks include: Designing and training
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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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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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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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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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of the CDL group and the postdocs, on closed-loop control systems with machine learning, and design for fabrication workflows. In addition to conducting their research, Ph.D. students at ITA get the
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committed to a sustainable and climate-neutral university . You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ, childcare
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. Above all, we are seeking highly motivated and enthusiastic scientists who thrive in collaborative working environments, enjoy taking on challenges, learning new skills, critical thinking and problem
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, electrical engineering, robotics, signal processing, and machine learning. We collaborate with several of them as well as institutions and companies in Switzerland and abroad. chevron_right Working, teaching