67 structures-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"LGEF" uni jobs in Switzerland
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and production enabled through digital manufacturing techniques. To our infrastructure belongs the Robotic Fabrication Laboratory (RFL), a unique digital construction environment, which allows for world
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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
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Ph.D. Position in Organic Chemistry, Polymer Chemistry, and/or Sol–Gel Chemistry & Materials Science
energy-related applications. Our research portfolio spans fundamental materials chemistry, process–structure–property relationships, and application-driven R&D, in close collaboration with academic and
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programming skills (ideally Python) and commitment to clear, reproducible, well-structured code Working knowledge of SQL and/or NoSQL databases (and motivation to deepen your expertise) Strong analytical
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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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(e.g. LangChain, vector databases, RAG architectures) is a plus Required people skills Self-motivated, structured, and curious about emerging AI technologies Strong analytical mindset with hands
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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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. Through cross-institutional collaboration and industry engagement, COMBINE provides structured doctoral training, secondments, and interdisciplinary research experiences. PhD position COMBINE-DC17 is hosted
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80%-100%, Zurich, fixed-term We are looking for a Research Engineer to join ongoing and future research projects at the intersection of machine learning, and structural design (e.g. trusses, space
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Thermal effects are a major source of geometric errors in modern machine tools. Accurate prediction of temperature fields inside machine structures is therefore essential for improving machining