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local activation), multi-timescale adaptation (local memory), and stimulus-specific adaptation (multi-task processing). While the co-optimization of dendrite-inspired functional circuits with emerging
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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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) 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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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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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your tasks: Developing optimization algorithms for massively parallel hardware architectures such as AI
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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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quantum computers, which represent a promising path towards fault tolerant and scalable quantum computing. The project addresses a broad range of optimization problems relevant to both science and industry
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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prediction Integration of domain decomposition methods into the learning framework to enable efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation