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off-site and on-site robotic additive manufacturing process for sustainable, low-carbon construction.To support the commercialization and technical development of this technology, we are looking for a
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operating and advancing the data platform, assist interdisciplinary projects that integrate multiple data sources, and use high-performance computing resources to manage and process large environmental
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computational and theoretical investigation of damage evolution associated with cavitation in soft materials under high-rate loading. The work will focus on developing physics-based models that connect behavior
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driven by curiosity about the physical mechanisms that underlie failure and by the ambition to translate this understanding into more reliable and resilient materials and structures. By combining numerical
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where the decision process is clear; (iii) fairness and non-discrimination: designing algorithms to make unbiased decisions by mathematically defining fairness (e.g., demographic parity, equality
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, Earth Sciences, Physics, Engineering, or a related field Strong quantitative and analytical skills as well as programming skills (Python required; experience with seismic data processing is advantageous
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of Competence in Research (NCCR) Separations. Job description Within a broader research program that integrates process modelling, computational materials screening and discovery, experimental synthesis, and
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The Computational Mechanics Group in the Department of Mechanical and Process Engineering of ETH Zurich is seeking one doctoral student. The position is funded by the SOFRA(CT) project and aims
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activities Prepare technical documentation, reports, and scientific publications Contribute to mission studies and proposal activities Profile MSc or PhD in Optical Engineering, Physics, Astronomy, or a
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for the physically informed processing and joint analysis of time-series data. The successful candidate will conduct observational programmes with the SPECULOOS facility and actively participate in the scientific