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system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models and machine
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the project will be the development of physics enhanced data driven methods to achieve reliable prediction of residual usable life of milling tools. The approach will be validated by application to industrial
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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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, and characterization of such devices. Responsibilities and qualifications The focus of this position is to help advance the development of a reliable and efficient dual-fuel HT-PEMFC using multi-physics
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The section for the Physics of Ice, Climate and Earth at the Niels Bohr Institute, the Complex Physics group at the Niels Bohr Institute, the Danish Meteorological Institute (DMI) and the Northumbria University
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properties of the extracted compounds, (iv ) scale-up the optimized extraction process for potential industrial application. This is a unique opportunity to contribute to sustainable food innovation while
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settings, autonomous systems need to adapt to changing requirements and conditions while ensuring highest levels of quality in the production process and safe interaction with human co-workers and other
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everything from basic science to strategic and applied research. The activities encompass research and education within materials, mechanics, physics, production, and industrial management and innovation
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The Section for Environmental Chemistry and Physics invites applicants for a PhD fellowship in advanced bio-oil analytical chemistry. The project is part of the research project “UPBIO”, which is
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models