187 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" positions at ETH Zurich
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incorporating machine learning. 2. Transcriptome Recording and Cellular History Reconstruction We are advancing our CRISPR-based transcriptional recording method (Schmidt, Nature, 2018; Tanna, Nature Protocols
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space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where conventional
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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields, and be at the beginning of their research career. Principal
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to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits
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contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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with the Sinergia project partners You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive
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100%, Zurich, fixed-term Human–Computer Interaction in Architecture and Digital Fabrication This fully funded, full-time PhD position spans four years and is embedded within the interdisciplinary
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, which not only supports your professional development, but also actively contributes to positive change in society You can expect numerous benefits , such as public transport season tickets and car
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Computational Design Lab and work at the interface of computer vision, computer graphics, hardware, and extended reality. The project is part of ETHAR, a new research initiative at ETH Zürich with a unique focus