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work will provide the "ground truth" for the project. By simulating complex inflow conditions, you will create the high-fidelity datasets required to validate the Wind Field Forecasting (WFF) models
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for load forecasting in scenarios where current models fall short, such as extreme weather events, grid incidents and high variability in renewable energy. You will explore techniques including graph neural
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and validating AI models for load forecasting in scenarios where current models fall short, such as extreme weather events, grid incidents and high variability in renewable energy. You will explore
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to extend the operational lifespan and reduce the overall weight of wind turbines. By innovating ways to lower mechanical loads on critical components and optimizing material usage, we aim to pave the way
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(e.g. via LLMs and RAG), tools for topic modelling, sentiment analysis, opinion mining, trend analysis, and various forms of text annotation need to be integrated in the infrastructure, as well as tools
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addressed is: What are the synergies and trade-offs among different SMR and yield-enhancing practices at farm and landscape levels under current and future climate scenarios? By integrating literature, remote
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validation of developed architectures Therefore, experience in numerical concrete modelling with commercial (e.g., Abaqus) and/or self-developed tools is essential. Experimental experience is a plus. Currently
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that aims to radically transform biomedical translation. If you are currently working outside academia, this is also a great opportunity. We strongly encourage you to apply. Your job You will contribute
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the theoretical framework developed on empirical data currently collected independently in the project. Examples of the approach can be found in Ecological Modelling 205: 196-208 and Ecological
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Key takeaways Rain erosion of wind turbine blades is one of the main causes for maintenance of (offshore) wind turbines. In order to increase lifetimes, leading edge coatings are developed