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Field
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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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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing
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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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to increasing CO2 and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber experiments, samples from world
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plants will respond to increasing CO2 and climatic change is a large uncertainty for ecosystems, crop productivity and climate predictions. To tackle this uncertainty, we combine: growth chamber
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uncertainty". The goal of this project is to advance our understanding of how humans are able to act in complex and uncertain circumstances, and how such knowledge can be applied to smooth our interaction with
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other sources to train and validate AI models. Develop computational workflows incorporating LLMs, Monte Carlo Tree Search (MCTS), phylogenetic inference, uncertainty quantification, and epidemiological
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), Proficiency with LCA software tools (e.g., openLCA, SimaPro, Brightway, Activity Browser), Experience with LCA uncertainty, sensitivity analysis, and scenario modelling, Energy systems modelling and simulation