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(IVT) at ETH Zurich intends to develop scalable optimization systems for operational support in large-scale road networks. Modeling and simulation are powerful tools for the development and validation
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Computer Science, Artificial Intelligence, or related field. Proven experience in machine learning and neural network architectures. Strong programming skills in Python and familiarity with PyTorch. Experience with
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being developed in close collaboration with key national partners, including the State Secretariat for Migration (SEM), the State Secretariat for Economic Affairs (SECO), and a wide network of
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; collaborating closely with molecular biologists, computational modelers, and clinical and preclinical research partners; documenting results clearly and contributing to publications and presentations; and
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, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients. Project background Our
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(SCI), lower back pain, neuro-degenerative disorders and neurological tumors. At the core of our research is the collaboration across disciplines spanning expertise in medicine, biology, computer and
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) programme on Artificial Intelligence and Machine Learning for Weather, Climate and Environmental Applications (E-AI). EUMETNET is a network of the 33 European National Meteorological Service, which exists
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The Pioneer Fellowship Deep-Tech Incubation Program is already a highly successful program for ETH spin-offs and has been running for 15 years. ETH start-ups, on the other hand, are technology companies based
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Professor towards Associate Professor in AI-oriented Computational Biologywith applications to Image Analysisor to Computational Molecular Evolution Starting date : 2025-26 Place : Lausanne, Switzerland
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, optimized for high-performance computing (HPC) environments. Classifying ice crystal habits using Convolutional Neural Networks (CNNs). Providing intuitive graphical interfaces for user interaction and data