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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific
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analysis, with possible specialisations in genomic and molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. This is based on perspective and
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Media (EDM), the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms, Implementations (RTG π³), the UBRA AI Center for Health Care , and the ZeMKI
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. The acronym CAUSE stands for Concepts and Algorithms for - and Usage of - Self-Explaining Digitally Controlled Systems. Digitally controlled systems are ubiquitous in our everyday lives, from transportation
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quantum processors using this technological platform design and implement optimization techniques for full-stack improvement of quantum algorithms model major sources of experimental error for control
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algorithms model major sources of experimental error for control theory or co-design methods Previous works can be found under the bibliographies of Dr. Felix Motzoi and and Dr. Matthias Müller: https