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the development of meta-optimization techniques that can automatically search for the best algorithm-hardware pair for a given problem. While we have a history of success in optimizing digital
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integrated circuit design, testing and simulation using Cadence is a plus Knowledge of evolutionary optimization methods is a plus Very good communication skills in English, both spoken and written. PGI-15
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 24 hours ago
vibrant research community in an open, diverse and international work environment # Scientific excellence and extensive professional networking opportunities # Salary and social benefits in accordance with
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Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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the Leibniz Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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offers access to state-of-the-art infrastructure and a vibrant scientific community. Join us in developing solutions for a rapidly changing world and help shape the future by working in an international
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applications as PhD Student Position (f/m/d) (Ref. 25/11) in the Leibniz Junior Research Group “Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible
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, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will play a central role in this interdisciplinary initiative. They will: Develop and apply machine
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the application of machine learning (ML) methods or large language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did