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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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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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that algorithmic parameters are tuned so that the over-approximation of the computed reachable set is small enough to verify a given specification. We will demonstrate our approach not only on ARCH benchmarks, but
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however require a significant measurement effort to be estimated, especially in the high-precision regime relevant for fault-tolerant quantum computing. You will investigate recent advances in
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, Helmut Schmidt University, and Hamburg University of Applied Sciences Teaching language English Languages All relevant courses are held in English. Programme duration 6 semesters Beginning Only
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The TUM School of Computation, Information and Technology at the Technical University of Munich (TUM) welcomes applications for a PhD or Postdoc Position (m/f/d, 100%, 2 years+) in Numerical Mathematics
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international research environment covering a wide variety of research areas, such as algorithms and data structures, machine learning, computer graphics and vision, database systems, artificial intelligence