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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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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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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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Training: Conduct Silvaco TCAD simulations (fabrication processes, device modeling, and circuit-level simulations) . Experimental Work: Participate in cleanroom processes, device fabrication, and electrical
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for Industrial Mechanics. This center is an integral part of the Institute of Mechanical and Electrical Engineering, situated in the vibrant city of Sønderborg. Together, they form a hub of technological
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PhD Scholarship in Biodiversity changes of marine flora and fauna associated with ecosystem resto...
estuaries. The research will have focus on the impact of restoration activities in Odense Fjord, Gyldensteen Coastal Lagoon and other Danish coastal waters. The selected candidate will be integrated in
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medicine. We offer a lively, engaged and innovative learning and study environment, which is closely integrated in the research environment. Our department has unique and advanced animal experimental
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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an efficient AI foundational exploration of the molecular space. How can we bias the generative models towards desirable molecular properties How can we integrate generative AI models and different molecular