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Field
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variablemodels), efficient inference methods(e.g. sampling methods), uncertainty quantification, calibration, and probabilistic forecasting, as well as the interdisciplinary application of these methods
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Iventa Austria Personalwerbung GmbH | Graz 12 Bez Andritz, Steiermark | Austria | about 2 months ago
development and analysis of probabilistic models (e.g. Bayesianmodels, latent variablemodels), efficient inference methods(e.g. sampling methods), uncertainty quantification, calibration, and probabilistic
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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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. Establish standards for controls, validation, uncertainty quantification, reproducibility, metadata, and experimental provenance. Evaluate AI-generated recommendations for scientific validity, feasibility
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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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confidently conclusions can be drawn. Develop and apply approaches for uncertainty quantification, robust inference, and validation of biomedical imaging results. Work directly with biologists, biomedical
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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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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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in AI for genomics (e.g., generative models, transformers, genomic language models, agentic AI) and related areas of statistics (e.g., uncertainty quantification for machine learning and AI). Apply
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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