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Field
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simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis, computer graphics, autonomous and assisted driving, 3D scene analysis
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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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predictive control under uncertainty for autonomous systems. The research aims to develop improved numerical methods for solving challenging belief-space motion planning problems, where the uncertainty in
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, global geopolitical dynamics and uncertainties in the supply of critical materials threaten energy system resilience. This transition relies on advanced technologies and renewable fuels. Therefore, unlike
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carbon capture simulation through advanced modelling tools—CapSim”. This project aims to improve CO2 capture simulation technology by apply state‐of‐the‐art techniques for evaluation of uncertainties in
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Research profile in machine learning (e.g. robustness, out-of-distribution/anomaly detection, fairness, explainability, uncertainty quantification) or AI applications in the healthcare domain Interest in
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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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-unique CO2
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reducing the uncertainty of national greenhouse gas (GHG) inventories. Duties/Responsibilities 70% Primary Research Focus: This postdoctoral associate will apply advanced knowledge-guided AI-modeling