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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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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
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development and marine management. Your primary tasks will be to: Compile and harmonize data from multiple sources (e.g., EMODnet, Copernicus, fisheries surveys, citizen science). Engage with data managers and