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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
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PhD Stipends within Distributed, Embedded and Intelligent Systems (DEIS) At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend is available within
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driving rapid growth in distributed energy resources (DERs), the electrification of transport, heating, and water systems — and the rise of hyperscale data centers. Coordinating these heterogeneous active
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that allow the development of context-aware, distributed, and embedded cyber-physical systems, with a particular focus on Internet-of-Things (IoT) and the computing continuum eras. Our vision is to pioneer
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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cutting-edge data science? And would you like to be part of a newly formed research collaboration between DTU and Novo Nordisk? Then you could be our new Postdoc. Read on to learn more! About the PhD
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect