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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 19 hours ago
the body. However, clinical PET imaging has so far been largely limited to imaging the distribution of a single radiotracer per scan. In collaboration with the Forschungszentrum Jülich, the PET department
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algorithms for large-scale or distributed training/Robustness, fairness, and personalization in multi-agent learning/Training efficiency and communication reduction/Distributed training of transformer models
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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of distributed computing, machine learning, image and text analysis, randomized data structures, high-performance computing, and quantum algorithms. Beyond this research, we aim to support computational thinking
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and/or statistical algorithms to classify building and land-use types relevant to electrical consumption Label and prepare training data for AI models; develop automated pipelines for classification
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microstructure (grain size, alloying elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
, scanning speed, layer thickness, scan strategy and subsequent heat-treatment) has a significant effect on the microstructure (grain size, alloying elements distribution, crystallographic texture), mechanical
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity
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elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted