17 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" PhD positions at Aalborg University
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demonstrate the ability to use this background to: Develop innovative decision-making strategies that seamlessly integrate operational research techniques and machine learning algorithms. Engineer solution
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of the ECHO-EMG research initiative, funded by the Independent Research Fund Denmark (DFF). The project aims to develop a novel system that combines high-density surface electromyography (HD-sEMG) and
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for behavioural and security properties; efficient algorithms for model checking, learning and synthesis; improved explainability and safety of machine learning models, e.g. by integrating neural and symbolic
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well as developing solution algorithms applying mathematical and computational approaches. The group has a particular focus on automated decision making in autonomous cyber-physical systems. Autonomous systems and
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research lines. Research line 1: "Digital twinning for 3D network optimization" focuses on developing distributed digital twin architectures and mechanisms for distributed network optimization in networks
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such 6-15 GHz into use and developing the needed radio resource management innovations to unleash its full performance potential. This includes integrated scheduling, link adaption, MIMO adaption
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management to research the fundamentals for a new era of power supply. Contribute to the development of innovative concepts and technologies that will not only make future power systems more reliable but also
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work will contribute to the development of a tool that will be able to help predict a site’s restoration success based on the DNA from 1g of soil. The Ph.D. candidate will be anchored in the Center
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to respectively certify correctness and incorrectness of neural-network controllers. We will also develop a framework that integrates these methods for constructing correct-by-design controllers. Methodologically
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for resilient manufacturing systems. This topic will build upon existing theory on modular and reconfigurable manufacturing systems and develop methods and model-based approaches to design and evaluate resilient