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waters. The proposed work can address any range of spatial and temporal scales, from microbial to global, but must cover questions related to reducing uncertainties in warming feedback on methane, provide
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to apply; in case of doubt, please reach out to PI Claus Lamm). Advanced experience in academic writing and with research methods (e.g. statistical analyses with R, Python etc.) Didactic competences
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of hybrid quantum-classical modelling approaches that support improved analysis, simulation, optimisation, and decision making under uncertainty. The role will suit a researcher with strong technical
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of hybrid quantum–classical modelling approaches that support improved analysis, simulation, optimisation, and decision making under uncertainty. The role will suit a researcher with strong technical
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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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and adaptive control under uncertainty and non-stationarity. The postdoc will be responsible for proving theoretical guarantees (e.g., convergence, stability, or sample complexity) for control tasks in
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warning can substantially reduce damage at relatively low water depths. There is considerable uncertainty around these damage functions and around the effects of such measures. This postdoc project
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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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strategies across Denmark, which includes but not limited to nitrification inhibitors and other fertilizer management strategies. A key part of this work will be performing uncertainty analysis and model
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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