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are insufficient for evaluating large disturbances such as faults and disconnections. This project focuses on developing and validating computationally efficient methodologies for large signal stability analysis
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and evolution of fault and fracture systems to crustal-to-lithospheric scale deformation responsible for the formation of sedimentary basins and mountain belts. This 3-year, full-time postdoctoral
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. Traditional small-signal stability analysis methods are insufficient for evaluating large disturbances such as faults and disconnections. This project focuses on developing and validating computationally
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and efficient data transmission, fault-tolerant communication, and navigational data integrity will also be explored. The ideal candidate has completed a PhD in mathematics, or related fields, with a
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computational resources that are needed, but also because it is prone to undetermined error propagation and validation and because it is intertwined with multi-scale, turbulent dynamics. Reliable experiments
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settings (non iid); - information-theoretic bounds on the generalisation error of learning algorithms; - estimation theory; - hypothesis testing in non-classical settings; - estimation and prediction in
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on projects in this area, with assistance and mentoring from senior academic staff familiarity with the theoretical methods of quantum error correction, and the theory of fault-tolerant quantum computing
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monitoring and fault detection. Collaborate with embedded systems and hardware engineering teams to integrate AI models into the BMS. Optimize AI/ML pipelines for resource-constrained environments, including
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keratinocytes and T cells with specific autoimmune disease relevance (SmB antigen specificity). Utilize advanced spatial transcriptomics technology (Multiplexed Error-Robust Fluorescence In Situ Hybridization
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needed to guarantee user-defined error bounds of reachable sets for nonlinear and hybrid systems. This project will exactly close this research gap: We will develop essentially new methods to ensure