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Field
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Job Purpose To make a leading contribution to a project on parameter inference, emulation and uncertainty quantification in fluid-structure interactions and growth & remodelling, as part of an EPSRC
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
, how it can be extracted, and how confidently conclusions can be drawn. Develop and apply approaches for uncertainty quantification, robust inference, and validation of biomedical imaging results. Work
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design spaces, long-duration simulations, and real-time applications. This PhD project will develop physics-informed deep learning and surrogate modelling approaches to accelerate simulation, uncertainty
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). Computational modeling of complex systems, including physics-based or data-driven approaches. Emerging challenges in modern numerical analysis, such as uncertainty quantification, high-dimensional problems
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and data-driven models [2, 3]. Metric Identification: Identifying key quality metrics for various "digital objects" throughout the ICME development lifecycle. Uncertainty Quantification & Propagation
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., DBH, height, crown dimensions, biomass, and stem quality) uncertainty quantification and propagation in operational forest inventory development of transferable inventory models across species, sites
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graph construction and mining, CDE development for data harmonization. Regulatory science and explainable AI, verification, validation, uncertainty quantification, and AI evaluation framework High
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, and algorithm design for inferring conclusions from multiple sources of information. Uncertainty quantification and propagation is vitally important such autonomous workflows, as is the development
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. • Familiarity with vibration data analysis techniques. • Experience with Monte Carlo simulation, uncertainty quantification, or sensitivity analysis. • Programming skills in Python, MATLAB, R, or similar
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: Physics-informed neural networks (PINNs) & surrogate modelling Time-series modelling & anomaly detection Bayesian methods & uncertainty quantification LLMs & multi-modal models Graph neural networks (GNNs