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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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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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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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foundations. Candidates should possess an exceptional academic record and a strong mathematical background. Experience conducting large-scale computational experiments (e.g., multi-GPU systems) is advantageous
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into industrial-grade, sustainable inks. PhD Position three: Computational Modeling of Ink Performance Model the Mechanics of Tomorrow’s Inks This PhD focuses on developing multi-scale mechanical models to predict
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multiple spatial and temporal scales. By adopting a multi-scale perspective, the project aims to significantly improve our ability to observe and quantify flood-related hazards. The main study area will be
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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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, interactive visual tools that can make complex process data accessible to a broader audience, enabling intuitive exploration and deeper understanding of multi-faceted process attributes, such as time, resources
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 6 hours ago
modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration) Strong programming skills in R or Python; familiarity with Bayesian
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation