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Field
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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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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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of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied
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Group (EASE IRTG), Empowering Digital Media (EDM), the Research Training Group HEARAZ , the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms
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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
-
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
-
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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workspaces under positional restrictions. Develop smart control algorithms that will allow the robotics end-effectors to communicate with the central control system and coordinate tasks with other end
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aims and objectives This project aims to develop an optimised, fault-tolerant implementation of the Falcon post-quantum digital signature algorithm for spaceborne FPGAs/processors. The key objectives