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mathematical and analytical models to predict coil loss, facilitating the optimal design of HPMCs Constructing a large-signal platform to measure coil loss of HPMCs Exploring innovative solutions, such as new
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computational humanities audiences. Qualifications Applicants should: Hold a PhD or equivalent qualifications in computational history, computational archaeology, computational social science, cultural evolution
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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water quality. You will be embedded within the Ethio-Nature project, funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use
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the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site nature-based solutions that enhance local
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potential and to optimize the chassis for protein production. The generated data will enable you to address research questions such as: Which traits are key for robustness, and can they be identified early in
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Postdoctoral Researcher Position in Digitalization of Metal Additive Manufacturing and CO2 Impact...
unique opportunity to bridge cutting-edge digital manufacturing technologies with real-world industrial sustainability challenges. If you are passionate about advanced manufacturing, process optimization