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Jülich who are leaders in their respective fields, viz. AI-driven materials property prediction and high-throughput materials development. Computational studies will be performed on Jülich’s world-class
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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separately, yet a reliable, open-source tool integrating a shallow-water solver and a multiphase porous-media solver within the same framework is missing. Without this coupling, it is not possible to predict
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of the Earth system at different temporal and spatial scales to improve predictive capability. Comprehensive education: Enjoy numerous opportunities for scientific training, skills development and problem
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with microstructural features and failure mechanisms Development of models to describe degradation mechanisms and predict component lifetime Presentation of research findings at project meetings
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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that define protein structures, functions, dynamics and interactions Protein structure prediction and modelling, e.g. in Rosetta, MODELLER, AlphaFold, etc. Protein-peptide complex prediction or docking
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Understanding of the principles that define protein structures, functions, dynamics and interactions o Protein structure prediction and modelling, e.g. in Rosetta, MODELLER, AlphaFold, etc. o Protein
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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predicts the interactions of the evidentials with different types of speech act and syntactic contexts. The position begins on October 1, 2025 (or shortly after) and will run until June 30, 2029. The ideal