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Field
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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system
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for geophysical flow modelling, uncertainty quantification, and high-performance computing. Its work relies on both deterministic and statistical approaches. The objective of this PhD project is to develop a new
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& Uncertainty Quantification Numerical Simulation & Multiscale Methods Numerical Linear and Multilinear Algebra High-Performance Computing Control and Optimization Computational Science & Engineering Applications
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-dimensional data. Develop modular methods that incorporate domain-specific information into latent-variable models. Investigate methodological questions related to computation, identifiability, uncertainty
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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the previous point 6, and additional specific requirements are: a) Consolidated experience based on scientific publications, in the characterization and quantification of functional compounds (polyphenols
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
with uncertainty quantification. However, this type of approach has not yet been used on problems as complex as an industrial piece of equipment. This thesis will be devoted to the use and development
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for external validation, transportability, subgroup performance, fairness, uncertainty quantification, and clinical utility assessment. Developing containerized, tested, documented, and version-controlled
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
against simulation outputs. Run targeted simulations to test specific mechanisms. Month 5 — Validation, uncertainty quantification, and extended simulations Validate models across wider parameter space
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that are inherently uncertain. Uncertainty quantification (UQ) seeks to characterize and propagate this uncertainty but translating it into actionable risk measures remains computationally demanding and