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by: Developing specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in
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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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contribute to the development of novel algorithms and methodologies that enhance the robustness and accuracy of acoustic measurements. We are looking for a highly motivated candidate, with a relevant MSc
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machine learning techniques to develop local graph representation models, which will be aggregated globally to enhance their predictive power and translational relevance, all while maintaining strict data
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Systems (DESS) research group and supervised by Professor Torben Bach Pedersen. Within Digital Energy, DESS has developed the award-winning FlexOffer technology, one of the few open, general, and scalable
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team. Significant software development experience in several key languages, e.g., Rust, C++, or Python (not MATLAB), algorithms, and machine learning is necessary as well as excellent communication
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potential market failures and prevention mechanisms. You will be combining theoretical analysis with practical applications, involving mathematical modeling, algorithm development, and coding. You should have
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale