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description Machine elements include the analysis and optimization of machine components and component systems based on performance, service life, energy efficiency, reliability, and environmental impact
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under different operational conditions. High breakdown voltages and low on-resistances are particularly critical for optimizing device efficiency and minimizing energy losses. Mechanisms of failure
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the analysis and optimization of machine components and component systems based on performance, service life, energy efficiency, reliability, and environmental impact. Particular emphasis is placed on issues in
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entitled “Non‑Alcoholic Beer Production Using Enzymatic Treatment of Barley Wort for Optimized Fermentation with Non‑Saccharomyces Yeasts.” The primary objective of this project is to demonstrate
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the development of methods and models refinements, analyses, and optimizations for scale-grid TES integration and operation planning algorithm. RESTORATIVE consists of 17 PhD students at 7 universities and 4
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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Multiple PhD positions in magnetism and spintronics are available within the Quantum Grenoble Doctoral Programme (3 different funding schemes). The call is open from 26 January till 16 March 2026
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NTNU and the application process here. About the position This PhD position will be hosted at the Department of Engineering Cybernetics (ITK), holding peak competences in decision-making, optimization
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peak competences in decision-making, optimization, control, and AI for cyber-physical systems. The position will be part of the research centre the Norwegian Center on AI for Decision (aiD) cooperating
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learning optimization at the Department of Computer Science, Norwegian University of Science and Technology (NTNU). The position is supported by the EU Horizon project CIRES and offers the opportunity