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PhD Stipend/Integrated Stipend in representation, compression, learning, and inference for classical and quantum data. At the Technical Faculty of IT and Design, Department of Computer Science, one
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms
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computer science or a related field, with an interest in semantic technologies (ontologies, semantic querying) and information extraction. It is important to have strong programming skills, to prototype
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power electronics to enhance the reuse and recycling of components, enabling a more circular life cycle. The work will contribute to a new paradigm in the development of power electronics that incorporate
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-intensive challenges that require coordinated advances from both data-science and electronics research. Wind turbines, especially prototype units—generate massive volumes of heterogeneous, high-frequency
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for this PhD project consist of combining empirical studies in healthcare environments, such as observations and interviews, with prototype design and development. The project will develop new knowledge on how
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their understanding of the project’s problem, the angle they want to take, and the expected challenges, as well as ideas to overcome them (max 6 slides). The slides can include both images and text and will serve as a
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of the project’s problem, the angle they want to take, and the expected challenges, as well as ideas to overcome them (max 6 slides). The slides can include both images and text and will serve as a starting point