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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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:32984792 [Lancet Digital Health 2020]), and network medicine (PMIDs:35572351 [Nature Aging 2021], 31375661, 30002366 and 30867426 [Nature Communications 2018, 2019a and 2019b]) to address challenging
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for Data 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
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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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of public law wih direct links to international law. Of particular interest are applicants who are interested in the Asia-Pacific region; technology and digital economy; sustainable development; cybersecurity
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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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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
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wearable devices using digital knitting and advanced fabrication techniques. Motion Sensing & Data Processing: Implement and optimize algorithms for joint motion tracking, integrating signal processing and
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law. We particularly seek candidates focusing on the Asia-Pacific region, technology and the digital economy, sustainable development, cybersecurity, international economic law, and international