188 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "University of St" positions at ETH Zurich
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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
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Center for Project-Based Learning. The successful candidate will contribute to research at the intersection of embedded machine learning, signal processing, and smart sensing systems, with applications in
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Armed Forces. The research contributes to the scientific foundation of monitoring and rapid altering systems for underground infrastructure. Project background Seismic and fiber-optic sensing technologies offer
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programming skills in Python Experience with machine learning systems or LLM-based architectures Experience working with complex data systems or developing applied AI prototypes Familiarity with modern AI tools
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journey, from the collection and management of data to machine learning, AI, and industrialization. With a large multidisciplinary team of professionals across three locations (Lausanne, Zurich, Villigen
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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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applying machine learning to PNT (Positioning, Navigation, and Timing) and geomonitoring challenges, including signal characterization and anomaly detection. Project background We are looking for a highly
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) Contribute to the strategic direction of research Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with
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methods and approaches are needed to better tackle the challenges posed by increased uncertainty and complexity. Machine learning (ML) and artificial intelligence (AI) methods have shown promise
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Systems.”Funded through an ETH Zurich Career Seed Award, this project aims to develop scientific machine learning frameworks that integrate physics-based modeling with neural network architectures. The goal