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or neural networks) on CFD data to develop a fast, data-driven wind field predictor. You will combine this surrogate model with AeoLiS and evaluate the accuracy of the new model setup by applying it to
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capabilities interact with operational conditions, human intervention and the wider maritime and port logistics systems. The postdoctoral researcher will develop data-driven and model-based approaches to support
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Postdoc Position in AI Foundation Models for Crop Microbiomes Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40 Application
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at least one of the following areas: crop modelling, plant sensing or data-driven crop management. Demonstrable experience in translating research insights into practical applications and collaborating with
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at least one of the following areas: crop modelling, plant sensing or data-driven crop management. Demonstrable experience in translating research insights into practical applications and collaborating with
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Postdoc: AI-driven Urban (Re)Design for Health Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36 to 40 Application deadline: 15
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Challenge | Overheating in existing dwellings due to increasing hot weather Change | Affordable, acceptable, effective nature driven facades Impact | Improved health and lower cooling energy demand
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Challenge: Predict long-term cardiac growth and remodeling in cardiovascular disease. Change: Develop multi-scale mechanobiological models for virtual human twins. Impact: Advance patient-specific
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Are you interested in the empirical foundations of flood damage modelling and keen to translate that knowledge into practical strategies for insurers to reduce losses through early warning and
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industry. Our group combines precision experiments, advanced imaging, and modeling to uncover the physics of tin droplets under extreme conditions of laser irradiation. We have established a strong track