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with big datasets: towards methods yielding valid statistical conclusions” led by Professor Xavier de Luna and Tetiana Gorbach (Statistics). The overall purpose of the project is to develop novel methods
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settings and in quantum serverless computing environments. The work will range from theoretical and algorithmic development of compilation and scheduling techniques, through the design and implementation
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position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Computer Science This Ph.D. project will develop and apply tensor-network and quantum-simulation
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cutting-edge systems design, AI at the edge, optimization, and shaping future mobile networks, this is your chance to dive in. A strong focus will lie on the development of optimization algorithms
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want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international
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topology, algorithms and complexity, combinatorics, differential geometry and general relativity, dynamical systems, mathematical physics, mathematical statistics, number theory, numerical analysis
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principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major
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knowledge and skills through interaction with their surrounding environment. Embodied AI requires tools, algorithms, and techniques to cope with real-world challenges including but not limited to uncertainty
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experience in radar research, developing signal processing algorithms for long-range ultra-broadband Synthetic Aperture Radar systems and short-range FMCW systems. In recent years, breakthroughs in
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at the Faculty of Engineering and contribute to cutting-edge research in radar systems. The radar group at BTH has extensive experience in radar research, developing signal processing algorithms for long-range