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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of additively manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon
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PhD student to establish recipes to provide post-plasma catalysis with different C2Hy distributions and different enthalpy content for optimization of post-plasma catalysis into C2H4. PhD candidate in
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Optimization, within the context of sustainable water-based cooling in megacities. The stipend is open for appointment from February 1, 2026. or soon hereafter. The duration of the position is three years
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is to investigate the optimal configuration for utility-scale AWE farms, considering the physics, economics, and environmental impact. To determine the optimal scaling strategies for kite farms, a
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impact of energy generation. This PhD project is part of the MSCA Doctoral Network AWETRAIN (Airborne Wind Energy TRAining for Industrialization Network). Its objective is to investigate the optimal
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1st, 2026 Work location: Geesthacht (near Hamburg) Application deadline: December 31st, 2025 EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization
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optoelectronic materials through advanced characterization and AI-guided optimization of epitaxial growth parameters. The PhD student will work on the various technological building blocks of the epitaxial growth
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materials and constructions. Use Material Flow Analysis (MFA) to explore optimal treatment pathways based on material composition. Collaborating with fellow doctoral researchers across the SCARPA network
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computational optimization of additively manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon, Geesthacht, Germany, with PhD degree awarded by Kiel
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renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks