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and communication systems. You will work with real measurement data and participate in both algorithm development and experimental validation. You will collaborate with industrial and academic partners
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communication infrastructure design and evaluation, reliable communication technologies, protocols and algorithms, internet of things and wired/wireless communication at all network layers. The candidate should
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control strategies based on Model Predictive Control (MPC) and real-time sensor feedback to dynamically adjust process parameters and recipes. The research will integrate data-driven models, material
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is to develop machine-learning-based algorithms for transmitter pre-distortion and receiver post-distortion architectures that enable distortion-free quantum communication systems. A key focus will be
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algorithms for physiological data Development of mobile applications for sensor integration and patient use Support in setting up cloud-based infrastructures for secure data collection and storage
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theory, and is expected to have experience with the practical implementation of control algorithms. Who we are Learning and Decision at AAU, founded in 2020, focuses on developing mathematical methods
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Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking, etc. Address nonlinear equalization in optical signal transmission and provide a comparison with neuromorphic electronics
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efficiency through parallelism (time-, frequency-, and mode-multiplexing), with a specific focus on photonic reservoir computing Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking
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Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking, etc. Address nonlinear equalization in optical signal transmission and provide a comparison with neuromorphic electronics
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, biomedical sensor and AI algorithm for cancer diagnosis. This project aims to develop a miniaturized robot and bio-sensor for on-site laryngeal cancer diagnosis. The candidate can contribute to one