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Field
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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-creation activities. Contribute to data preparation, sustainability indicators development, and multi-criteria decision-making analyses for scaling RCBMs across Europe and China. Utilize SWOT method
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holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement
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renewable energy systems playing a pivotal role in transitioning away from fossil fuels. Wind turbines, solar photovoltaics and hydropower form the backbone of low-emission (“green”) electricity generation
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holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement
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can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried out within the DFG Priority Programme “DaMic - Data-driven Alloy and
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analysis framework that supports flood-resilient urban design by combining multi-sensor Earth observation (EO) data, hydrological–hydrodynamic models, urban digital twins, and advanced AI methodologies
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a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a holistic multi-hazard risk
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. This includes research on psychological assessment, psychometrics, and methods in large-scale assessments. Your tasks We are seeking an international visiting researcher to collaborate on an innovative research
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provides broad knowledge of a variety of research fields, energy scales and complexities required for the theoretical and experimental description and interpretation of the involved systems in a fundamental