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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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and simulation aspects across a wide range of fields - from biomechanics and geophysics to polymer-fluid coupling. Further areas of interest include numerical algorithms for high-dimensional problems
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Learning Algorithm for Grid Optimization linked to Bayesian uncertainty outputs Test bidirectional interaction: Bayesian updates → Reinforcement Learning policy adaptation → grid performance feedback
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-omics data sets generated with innovative high-throughput technologies used in Research Sections I and II (e.g. sensory, metabolome, proteome and transcriptome data) by using efficient algorithms and
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are developing algorithms and tools to address these bioimage analysis problems, which are all driven by real biomedical research. Depending on the background and interest, the student will have the opportunity
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system requires effective orchestration that can schedule the application on these systems. While traditional scheduling algorithms exist, these do not focus on the energy footprint of applications
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you will do Driving innovative AI research through the development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine
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concise code Diligence in implementing, testing and documenting algorithms and results Curiosity about a deeper understanding of Deep Learning architectures What you can expect Fascinating challenges in a