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) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
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-dominated parametrized problems, control, and uncertainty quantification. The start date is flexible and the initial appointment will be for one year with yearly extensions depending on performance and
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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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. 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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models, network analysis, graph-based learning, uncertainty quantification, scientific machine learning, or interpretable AI. This position is full time, on-site at the Penn State University Park campus
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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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importance, model selection, trait prediction, and uncertainty quantification. Substantial professional development opportunities are available, including travel and speaking opportunities at international
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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
approaches, provide a natural bridge between experiment and mechanistic understanding. Data-driven methods, for instance for inverse parameter identification or uncertainty quantification in fracture mechanics
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
. Data-driven methods, for instance for inverse parameter identification or uncertainty quantification in fracture mechanics datasets, are not an end in themselves, but a compelling extension where they