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-tasks and language models. This position is part of SDU’s initiative to develop energy-efficient AI accelerators based on alternative model architectures that cannot be leveraged as efficiently
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SQL databases and file repositories. We are now taking the next strategic step: developing ontologies and a dynamic knowledge graph to semantically link our internal data systems - and connect them
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in our laboratory (see Nielsen et al. 2024, Nature). We also strive for implementing nano discs from polymers or MSPs to extract or stabilize hDAT. The main task is to solve multiple structures of hDAT
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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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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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optimization. Experience with energy system modeling - ideally of large scale multiple country energy systems, PtX and renewable fuel production. Strong writing and presentation skills. A willingness and desire
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scale multiple country energy systems, PtX and renewable fuel production. Strong writing and presentation skills. A willingness and desire to engage in interdisciplinary collaboration and teaching. Good
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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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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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Job Description Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges? Responsibilities and