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Field
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on ensuring transparency through citation of source material and developing methods to quantify statistical uncertainty in generated outputs. The post-holder will collaborate with a multidisciplinary team
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to advance the development and application of reliable, interpretable and uncertainty-aware machine learning methods for high-stakes regulated domains, including law, finance, policy and regulatory decision
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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in interdisciplinary fields, knowledge of codes in related disciplines, such as thermal hydraulics, actinide chemistry, fuel cycle analysis, particle physics, or uncertainty quantification. A minimum
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. This position is designed for scientists with strong computational and quantitative training who are interested in agent-based modeling, network science, infectious disease dynamics, uncertainty quantification
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to substantial measurement uncertainty. The group is particularly interested in latent variable modelling approaches to such settings, where the key quantities of interest are not directly observed and
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Qualifications Experience with graph neural networks, machine-learning interatomic potentials, or related scientific machine-learning methods for atomistic systems. Familiarity with uncertainty quantification
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
feedbacks and implications of different decarbonization pathways under uncertainty. Will contribute to one or more of the following areas: advancing methods and computational techniques for human systems
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feedbacks and implications of different decarbonization pathways under uncertainty. Will contribute to one or more of the following areas: advancing methods and computational techniques for human systems
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knowledge recombination, conceptual distance, topic novelty, and uncertainty. The project will mostly rely on econometric analysis of large-scale microdata sets for researchers in Denmark, in particular, high