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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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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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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
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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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-mobility, automation, climate solutions, and energy efficiency. Research at the Centre encompasses a diverse array of fields such as cyber-physical systems, mechanical structure integrity, renewable energy
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degrees in either the natural sciences (chemistry, physics, mathematical/computational biology) or in the formal sciences (statistics, computer science, mathematics), but must have a serious interest in