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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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research environment, with a potential to work with a Quantum Computer through our collaboration partners. The Center possesses the unique possibility to investigate cutting-edge interdisciplinary questions
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Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms. Strong publication record in top-tier venues. Solid background in one or more of the following: Query processing
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on representing the structural response Physical experimental testing for structural and geotechnical applications Data acquisition and processing from monitoring systems Validation of modeling results against
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
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. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or other related department to apply. The successful applicants will design controllers for a variety of
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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. Contribute to the smooth operation of the laboratory. Participate in the academic activities of the division. This position does not include teaching duties and the SSEL provides financial support for travel
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on representing the structural response Physical experimental testing for structural and geotechnical applications Data acquisition and processing from monitoring systems Validation of modeling results against
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natural language processing and machine learning workflows; (3) experimental design and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal