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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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and exchange. These platforms provide data for flexibility and demand response, connecting distributed resources across decentralized ownership and spatial boundaries. By making energy interactions
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between things, developing the mathematical and computational foundations for optimal information transfer between surfaces and volumetric current distributions in complex scattering media. The research
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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a scenario generator for
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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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to develop and apply advanced remote-sensing approaches and AI-assisted image analysis to investigate the distribution, diversity, and spatial dynamics of Antarctic lichen communities, thereby contributing
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Job Description DTU Electro invites applications for a PhD position in digital signal processing and machine-learning-enabled continuous-variable quantum key distribution (CV-QKD). The position is
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generate, transmit, and detect OAM-entangled photons under realistic atmospheric turbulence. Deep learning algorithms will be employed to pre-compensate distortions in real time, maximizing state fidelity