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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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. You should have a strong academic background in engineering, applied mathematics, or computer science, combined with a clear interest in scientific programming, machine learning, and data analytics
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science, culture, and learning. The Centre benefits from SDU’s strong industry connections in Southern Denmark and Northern Germany, including collaborations with leading companies in sectors like e
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on university-level teaching and learning as part of the USE project. The positions are connected to the USE centre (University Science Education ) which is a newly established initiative funded by the Novo
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electronic components including SiC/GaN based devices. The candidate will also have opportunity to work with industry related problems. Key Responsibilities Research & Learning: Develop expertise in advanced
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components including SiC/GaN based devices. The candidate will also have the opportunity to work with industry related problems. Key Responsibilities Research & Learning: Develop expertise in advanced control
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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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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learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three systematic phases
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digital twin capabilities to all respective levels of the manufacturing system, combining machine learning, decision-making, and optimisation with Digital Twins. This should allow systems to dynamically