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questions about the recruitment process, please contact HR Coordinator Mette Fisker Præstegaard, Email: mfp@au.dk Place of work Department of Political Science, Bartholins Allé 7, 8000 Aarhus Formal
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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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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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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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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here . Applications received after the deadline will not be
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will be informed whether they have been shortlisted for assessment or not. The hiring process at Aalborg University may include a risk assessment as a tool to identify potential risks associated with new
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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of the project makes it a prerequisite that you have a broad interest in the application of physical methods for the study of (bio)chemical function. Your primary tasks will include to: develop, evolve, and apply
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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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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