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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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. 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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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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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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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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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