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Field
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, contributing to the development of innovative spatio-temporal analysis and engineering challenges associated with uncertainty. The role supports collaborative investigations, model development, and experimental
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
. The Research Atmospheric clouds and aerosols are major sources of uncertainty in weather and climate prediction due to their complex, multi-scale nature — from nanometers to hundreds of kilometers. Turbulence
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
Experience in uncertainty quantification or statistics applied to quantum chemistry and machine learning would be advantageous For more details, please take a look at the role profile. We'll still consider
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constraints. Experience conducting sensitivity analyses, generating KPIs, and managing simulation uncertainty. Interest in collaborating across disciplines (electrical, mechanical, policy, IT) and ability
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from electricity generation for charging EVs, buses, trucks, rail, pavement resurfacing, tire and brake wear, and supply chain effects. Conducting life-cycle uncertainty assessments of building materials
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erosion and sedimentation processes, aiming to compare results with existing stratigraphic data and drainage evolution models. * Quantitatively assess data uncertainties and result errors throughout
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from electricity generation for charging EVs, buses, trucks, rail, pavement resurfacing, tire and brake wear, and supply chain effects. Conducting life-cycle uncertainty assessments of building materials
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negotiable, preferably in autumn 2025 or in 2026. Background Northern wetlands emit large amounts of methane (CH4), a potent greenhouse gas. There are high uncertainties in the estimation of wetland CH4
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experts. Good communication skills. . Nice to have experience: Statistical knowledge: Experience in uncertainty analysis, particularly in the context of large datasets and real-world statistical modelling
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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing