205 structural-engineering-"https:"-"https:"-"https:"-"https:"-"LGEF" positions at ETH Zurich
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Lugano and in Zurich. For this position the preferred work location is Zurich, but it could also be Lugano. Project background We are seeking a driven software engineer to work at the intersection
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the geological structures and potential reservoir Integrating extensive passive seismic recordings and borehole seismic data (including vertical seismic profiling, VSP) into the imaging and characterisation
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the structure and function of plant pathogen genomes. We are looking for someone with prior experience in either fungal or transposon comparative genomics, who can explore the diversity and evolution of Starship
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concepts into clear, engaging visuals - working closely with our engineering team to bring ideas to life. The scope of the work could be 60% for one project or with the right candidate and skill set extended
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, electrical engineering, a natural science, medical, or related field Experience in scientific research, ideally in the area of machine learning and/or data science Strong organizational skills and an
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and engineering the structure, function, and dynamics of microbiomes across diverse biological and environmental systems. Several research groups at the Institute of Microbiology contribute to the NCCR
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for world class research in the field of robotic fabrication in architecture and construction. Job description We are looking for a highly skilled software engineer to work on the development of python
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opportunity to work with leading experts across Europe to design and deliver sustainable meta-materials for vibration mitigation, self-aware meta-components and carbon-efficient meta-structures
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The Albert Einstein School of Public Policy at ETH Zurich is an interdisciplinary centre that combines public policy, science and technology to address the greatest societal challenges of our time
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of semiconductor devices. The aim is to predict these properties for arbitrarily large structures, at a DFT-level of accuracy. As a starting point, you will extend the large-scale equivariant GNNs we develop