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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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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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of self-assembly for specific types of molecules. Here, we use symmetry and the geometric properties of the molecules in order to calculate bounds that help to predict specific behavior. Moreover, we would
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. Here, we use symmetry and the geometric properties of the molecules in order to calculate bounds that help to predict specific behavior. Moreover, we would like to more widely explore the possibility
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
self-assemble from a number of interacting single-stranded DNA molecules. An accurate prediction of DNA structures still remains difficult, which significantly slows down the development of new desirable
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fields, viz. AI driven materials property prediction and high thoughput materials development. Computational studies will be performed on Jülich`s world-class computational and AI infrastructure. Your
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heterogeneous and opportunistic sensor networks. Therefore, such an approach may significantly improve rainfall and runoff predictions. Research goals: Our primary goal is to improve the accuracy and prediction
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formation of two- and three-dimensional DNA structures which self-assemble from a number of interacting single-stranded DNA molecules. An accurate prediction of DNA structures still remains difficult, which
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Multidimensional Omics Data Analysis You will be responsible for Prediction of metabolic activity in complex microbial communities, leveraging
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tunnel testing and statistical modelling using extreme value theory, the project will yield predictive frameworks for structural risk under rare yet damaging wind conditions. The results will support