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Field
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validate data-driven methods that combine production data, predictive and state-estimation models, uncertainty-aware learning and constrained optimization to improve process consistency under process
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is looking for a Research Fellow to conduct research on AI-enabled discovery and development of advanced functional materials and coating processes, with emphasis on data-driven modelling, uncertainty
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infrastructure systems under uncertainty. We are seeking a Research Fellow to develop advanced computational methods for the reliability and robustness assessment of structural and infrastructure systems. The
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; analyzing uncertainty and failure modes; and preparing research code, documentation, and manuscripts. Equal Employment Opportunity Statement All qualified applicants will receive consideration for
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; analyzing uncertainty and failure modes; and preparing research code, documentation, and manuscripts. Equal Employment Opportunity Statement All qualified applicants will receive consideration for
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validating them against datasets generated by collaborators. The candidate will use surrogate modelling and data-centric approaches to complement simulations, incorporating uncertainty to evaluate system
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, modelling source geometry and earthquake occurrence, quantifying epistemic uncertainty, and building auditable logic-tree inputs for probabilistic seismic hazard assessments (PSHA). Key Responsibilities
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, uncertainty and applicability across microstructures and environmental conditions. Publish research findings, contribute to project reports and mentor students. Job Requirements: PhD in Materials
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approaches to complement simulations, incorporating uncertainty to evaluate system resilience, and developing useful tools that will enable industry to adopt energy micropiles as a dual structural
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methods for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi