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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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techniques and the creation of a spectrum digital twin. You will also carry out experimental/proof-of-concept implementation work to demonstrate the applicability of your research. Your main research
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UiO/Anders Lien 17th August 2025 Languages English English English PhD Research Fellow in Formal Methods for Data Protection in Digital Twins Apply for this job See advertisement About the position
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Protection in Digital Twins Apply for this job See advertisement About the position Position as PhD Research Fellow in formal methods for data protection in digital twins is available at the Department
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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given to applicants with: Hands-on experience with digital twin simulation platforms, edge computing, and smart factory infrastructure. Background in data acquisition, signal processing, or real-time
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to international law. We particularly seek candidates focusing on the Asia-Pacific region, technology and the digital economy, sustainable development, cybersecurity, international economic law, and international
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are a major part in implementing the 2030 vision of the Faculty of Medicine, i.e. to become leading within digital health and well-known for doctors and engineers finding solutions together. Our mission
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of predictive AI models using multimodal data (e.g. time-series signals, neuroimaging, clinical records) for healthcare applications. Contribute to the development of a Digital Twin of the brain's motor system
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Computer Science, Electrical & Electronic Engineering, or equivalent. Background knowledge in signal representation/processing, data-driven and machine learning/analysis, esp in climate related topics. Prior