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Field
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relationships between vertical urban form and environmental conditions, assess uncertainty and transferability across the three cities, and develop baseline-to-2030 and 2030–2050 scenarios of vertical development
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system surrogates, or uncertainty quantification. Model Predictive Control (MPC), data-driven control, or hybrid model-based / data-driven controller synthesis (e.g., RL-MPC) for complex dynamical systems
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under uncertainty. The role will suit a researcher with strong technical capability in quantum computing, data-driven modelling, infrastructure systems, computational optimisation, simulation
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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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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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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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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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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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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