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Field
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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
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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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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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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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-sampling data. Furthermore, the position holder will play a central role in creating high-quality training datasets (seagrass maps) to support artificial intelligence (AI) algorithms used in related projects
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promising alternative to classic methods of process optimization due to its adaptive decision making and data efficiency. In this thesis, a BO algorithm for the optimization of laser processes is to be
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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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algorithms to analyze OMICS data (e.g., genome, transcriptome, proteome, microbiome) from patient samples and basic research perform single-cell RNA-Seq and spatial transcriptomics analysis apply artificial