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Field
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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at the University of Southampton, will model the micro-scale behavior of individual fibers within the matrix (interfacial shear strength) to predict the composite’s macro-scale behavior against experimental values
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12.10.2021, Wissenschaftliches Personal The TUM Professorship for Data Science in Earth Obervation is seeking a full-time PhD candidate on the topic of “Multi-scale Semantic Understanding
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of the following tasks: Extensive literature review on the multi-scale model for strain-induced phase transformation of stainless steels and the influence of tramp elements and contaminations on the microstructure
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experimental chemistry. Additional training in a wide range of soft and hard skills is available at the University. What you will do: This PhD project aims to bring together multi-scale modelling and data-driven
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-speed fiber optic telecommunications are now entering the era of multi-core fibers to meet the world’s ever-increasing demand for data transfer. Signal regeneration is a key function of future networks
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. Correlating experimental, ab initio and multi-scale simulation as well as machine learning techniques is central to our mission: Development and application of advanced simulation techniques to explore and
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of multi-petawatt laser technologies. The primary objective of the project is to exploit the theory and large-scale numerical simulations to design a laser-based optical system capable of efficiently
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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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cutting-edge methods in chemical, mechanical, and plasma processing of metal ores for a truly circular economy. Correlating experimental, ab initio and multi-scale simulation as well as machine learning