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) position in Materials Engineering with focus on computational optimization of additively manufactured Mg alloys for biodegradable implant applications PhD position - Ad Print4Life DC12 - code no. 2025/MO 4
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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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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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An error occurred during a connection to www.hereon.de. The OCSP server suggests trying again later. Error code: SEC_ERROR_OCSP_TRY_SERVER_LATER What can you do about it? The page you are trying to view cannot be shown because the authenticity of the received data could not be verified. Please...
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, enabling algorithm–circuit co-optimization across the computing pipeline with respect to key metrics such as power consumption, computational delay, and area efficiency. Beyond circuit prototyping
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The group ofSebastian Stich works on the design and analysis of optimization methods
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17.11.2025, Wissenschaftliches Personal The Chair of Computing in Civil and Building Engineering (CCBE) at Technical University Munich invites applications for the position of a Research Assistant
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exclusively in German. Full-time / part-time full-time Programme duration 8 semesters Beginning Only for doctoral programmes: any time Application deadline Please look at our website for the current application
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used