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Field
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. You will contribute to the following areas: Review and benchmark datasets used for initialization, calibration, and validation of GCMs, identifying sources of uncertainty and quantifying their impact
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate predictions Ability to work in a team Ability to communicate orally in
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, tire and brake wear, and supply chain effects. Conducting life-cycle uncertainty assessments of building materials, carbon sinks (e.g., carbon dioxide removal options), and alternative low-carbon fuel
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shortlists of promising molecules with quantitative estimates and uncertainty ranges; and close iteration with experimental partners to validate predictions and refine models. The position also includes
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uncertainty quantification. Machine learning will be applied to identify when, where, and why forecasts can be considered forecasts-of-opportunity. This position seeks candidates with a background in
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environment. • Excellent written and oral communication skills. Desirable Knowledge, Skills, and/or Abilities • Familiarity with multi-physics modeling frameworks. • Experience with uncertainty quantification
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ice sheets and associated uncertainties that will inform the next report of the Intergovernmental Panel on Climate Change (IPCC). About the position The overall aim of this postdoctoral project is to
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and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will
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to analyse datasets Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models