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fabrication methods (robotics) while integrating real-time lifecycle data into decision-making that substantially reduces the construction phase’s environmental impact. The selected candidate will work on DTs
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Join us at the Department of Electrical and Computer Engineering at Aarhus University to help shape the future of sustainable water management. We’re looking for a motivated postdoc for a 1-year
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. Join us at the Department of Electrical and Computer Engineering, Aarhus University, where we are developing semantic-aware communication that leverage edge AI, semantic reasoning, and efficient time
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at the intersection of advanced probabilistic machine learning and microbial bioscience. This position offers a unique opportunity for developing novel probabilistic ML methods with a view towards
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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of TSN-based in-vehicle networks. These networks carry mixed-criticality traffic and use TSN with multiple traffic shapers and redundant communication. You will investigate methods for runtime analysis
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transformation Publish research results in top-tier peer-reviewed journals and present at international conferences. Innovative integration Research and integrate new methods and technologies to merge existing
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Join us at the Department of Electrical and Computer Engineering at Aarhus University to help shape the future of sustainable water management. In close collaboration with the Hydrogeophysic Group