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, enabling algorithm–circuit co-optimization across the computing pipeline with respect to key metrics such as power consumption, computational delay, and area efficiency. Beyond circuit prototyping
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-related traits, and heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck of computational load for such a
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reliability through systematic validation methods. CAVECORE combines cutting-edge research in AI-enabled robotics with novel evaluation frameworks aligned with EU AI Act requirements. More information: https
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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partner institutions: Finnish Graduate School Helsinki, University of Stockholm, University of Graz, University of Bergamo, Catholic University of Lisbon, or University of Warwick. For more information, see
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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is hosted at the Chair for Algorithms and Complexity, headed by Prof. Susanne Albers (http://wwwalbers.in.tum.de/index.html.en). The dissertation work will involve research in the fields
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The Fraunhofer Institute for Algorithms and Scientific Computing SCAI in Sankt Augustin, near Bonn, has around 180 employees who research and develop innovative methods in the field of computational
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing