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highly motivated PhD student to join a DFG-funded international project that investigates plant - microbiome interactions through large-scale metabolomics and other - omics platforms. Your tasks: Process
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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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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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dynamics and composites? Are you familiar with numerical simulation and optimization? You got some experience with data evaluation and analytics? Maybe you even have some knowledge of the wind industry 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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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