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developing and deploying an ocean‑monitoring system for offshore installations. The research will integrate optical and acoustic sensors, autonomous data collection, and advanced perception methods to track
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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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complexes. Nat Commun. 9(1):2311. Lab and Research Environment You will be part of the research group led by Assoc. Prof. Rasmus Siersbæk (Siersbaek group ) at the Dept. of Biochemistry and Molecular Biology
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Research Environment You will be part of the research group led by Assoc. Prof. Rasmus Siersbæk (Siersbaek group ) at the Dept. of Biochemistry and Molecular Biology (BMB) at SDU. The Siersbæk group is part
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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
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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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. Your work will focus on developing physics-informed AI methods to enhance decision-making in design and operation of next generation thermal energy storage systems, such as latent heat TES and
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academia and industry. You will be involved in the “DTU Alliance” project in collaboration with Prof. Anna Scaglione at Cornell University, with the opportunity to undertake a research stay of 5–9 months