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demonstrated experimental realizations and proven theoretical advantages. The project may involve several aspects, including mathematical theory, algorithm development, error correction, adaptation of GBS-based
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: Odense, 5230, Denmark [map ] Subject Areas: mathematical theory, algorithm development, error correction, adaptation of GBS-based algorithms to other quantum computing platforms, and the development
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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water, etc.) by fusing sensor data (e.g. sonar, INS, USBL). Collaborative control strategies: Developing coordinated control algorithms that allow the USV and UUV to perform joint missions without
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detailed local inspection. Digital twin simulations: Developing simulation environments replicating sensor characteristics and anomaly conditions to test perception algorithms under controlled, repeatable
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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of the ECHO-EMG research initiative, funded by the Independent Research Fund Denmark (DFF). The project aims to develop a novel system that combines high-density surface electromyography (HD-sEMG) and
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life