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Field
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Faculty of IT and Design invites applications for a PhD stipend on the topic of Cooling Flexibility Modeling, Estimation, Aggregation, and Optimization, within the context of sustainable water-based cooling
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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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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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methods and design strategies to optimize polarization purity in active array antennas. By addressing these challenges, the candidate will contribute to the next generation of high-performance, active
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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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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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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