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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
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complementary methodologies (corpus data and offline experimental measures). On the theoretical side, the project will develop a formal compositional model that generates the observed parameters of variation and
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contributes to the improvement of climate prediction models. The Atmospheric Chemistry and Atmospheric Microphysics departments are looking for a committed doctoral student to carry out this project. You can
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to describe ocean turbulent fluxes #developing theoretical and conceptual models to understand and predict ocean mixing #work as an integrative part of a motivated multidisciplinary team within the institute