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, explores opportunities to design a high-performance RISC-V-based platform to support analytics and AI applications. Part of this research, aspects of cybersecurity for data collection and processing would
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welcomes applications from individuals with a master’s degree in the following fields: Computer Science Mathematics Natural or engineering sciences or an equivalent degree, interested in High-Performance
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through their investor conference a co-investment from the College of Science and Engineering. This funding provides a unique opportunity to advance innovative medical device research and support four high
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science. The focus of this position is on
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many real-world applications, finding a solution requires high-performance computer clusters that consume large amounts of energy and run for a long time. Our project aims to create a radically new
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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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PhD fellowship in Computer Science, PhD Project in Privacy and Cybersecurity Department of Computer Science, Faculty of SCIENCE, University of Copenhagen The Programming Language and Theory
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular