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Modelling ore fabrics along comminution to predict liberation. Your tasks Develop a methodlogy to predict breakage and liberation, including: Develoment and implementation of parametric, fast preferential
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learning for dementia prediction. see here: https://ai-med.de/wp-content/uploads/2025/04/PhD_Position.pdf PhD Position: Interpretable Models for Dementia Prediction Lab for Artificial Intelligence in Medical
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to the quantum level. In the focus are advanced techniques for the preparation of controlled atomic, molecular and cluster ensembles, combined with modern ultra-short laser techniques, as well as a variety of
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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
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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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of the characterisation techniques used in the field obtain average properties of what in reality is an ensemble of molecules. The aim of this project is to study the influence of molecular disorder on the light emission
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understanding of the key factors that affect their performance is limited by the fact that most of the characterisation techniques used in the field obtain average properties of what in reality is an ensemble
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, you will develop highly accurate computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably
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developing theoretical and conceptual models to understand and predict ocean mixing work as an integrative part of a motivated multidisciplinary team within the institute and with external academic partners