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. This project aims to develop scalable, textile-based wearable devices for real-world joint motion tracking, enabling continuous and unobtrusive monitoring of movement in clinical, rehabilitation, and everyday
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analyses in nonclinical drug development. The postdoctoral role involves designing and implementing algorithms for anomaly detection, segmentation, and classification to contribute to the development
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and confidence as they digitalize. For more details, please view https://www.ntu.edu.sg/dtc Key Responsibilities: Conduct advanced research in Generative AI, focusing on the design, development, and
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data
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both the ATLAST telescope and the nearby community of San Pedro de Atacama. The PhD candidate will have the unique opportunity to shape the renewable energy management system by developing novel real
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applications in healthcare. Duties and Responsibilities Develop AI/ML Models: Create models for neuroscience and neuromorphic computing. Simulate Brain Systems: Model brain molecules, cells, and circuits
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innovative material solutions. This project tackles this challenge head-on. The project vision is to develop a pioneering AI-driven methodology for designing Functionally Graded Materials (FGMs) specifically
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at the University of Bergen. The Department hosts the Center for Digital Narrative (CDN) , a Norwegian Centre of Research Excellence. The Center focuses on algorithmic narrativity, new environments and materialities
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the renewable energy management system by developing novel real-time control & optimization algorithms. Pilot demonstrations on partner radio telescope facilities such as APEX and the Sardinia Radio Telescope may
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning