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management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
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: Approximately 2,000 EUR/month for three years Website: IMPRS-ESM Application Contact: office.imprs at mpimet.mpg.de The International Max Planck Research School on Earth System Modelling (IMPRS-ESM) invites
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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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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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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
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on opportunistic observation data and various influencing factors (e.g. meteorological, climatic). The final step is to contribute to the development of a forecasting model that will predict the pollen drift