215 structural-engineering-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" uni jobs at ETH Zurich
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is to build efficient and robust computational tools for analyzing complex engineering systems. Applications include structural dynamics and other dynamical systems relevant to real-world engineering
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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100%, Basel, fixed-term A World-Class Research Environment at the Nexus of Biology, Engineering, and Physical Sciences The Biotechnology and Bioengineering group led by Prof. Dr. Martin Fussenegger
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knowledge and technology from academic research into the Swiss mechanical, electrical, and metal industries. The Machine Concepts research group at inspire AG is offering a position in the field of Industrial
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, μSR and other techniques. Geometric frustration in quantum systems leads to a tight competition between qualitatively new collective spin states, such as topological structures, quantum spin liquids
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Required Experience: PhD in Geodesy, Geomatics, Aerospace Engineering, Signal Processing, or a related field Proven experience in GNSS data analysis and processing Very good programming skills (e.g., Python
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Science at the ETH Zurich Institute of Environmental Engineering, with expertise in water chemistry assessments in forested ecosystems. The Silviculture group is part of the Chair of Forest Resources
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and within the ENHANCE Alliance. Job description You will support the S&E Initiative in the following focus areas: Assisting with website editing, including preparing, structuring, and publishing
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thinking with a structured, quality-focused approach to data and methods. Ideally, experience in one or more of the following: data engineering, building data-driven apps, computational linguistics, machine
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industrial partners to tackle advanced modelling, simulation, sensing, and data analysis challenges in engineering systems across sectors. Project background The COMBINE Doctoral Network aims to train a cohort