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is to develop a low-power, privacy-preserving Internet of Things system that supports caregivers in delivering high-quality, home-based care for elderly individuals. The project focuses on developing
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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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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small-scale processing sector. By joining this project, you will contribute to the development of AI-powered tools that predict non-compliance, improve food safety monitoring, and ultimately protect
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funding affect the subsequent performance of firms and scientists, in terms of outputs such as the number of papers, products, patents, etc. (Can an optimal applicant template be developed by training
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the interfacial phenomena between water contaminants and adsorbent materials. As a member of the “Nano-Micro-Macro. Structure in Materials” research group, led by Prof. Joerg Jinschek, you will push the boundaries
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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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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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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