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scenarios for achieving a low-carbon society in 2050. A key ambition of the project is to support scenario-based planning and backcasting. Rather than predicting a single future, the modelling framework
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detailed knowledge of the performance parameters that affect their application software, as it aids in making future technology choices, predicting performance and scalability, and adapting critical software
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authenticity of visual media and provide understandable evidence for model decisions. The candidate will investigate how general-purpose pretrained visual and multimodal representations can be adapted
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. This includes extending or adapting existing scenario modelling approaches to support increasingly complex cybersecurity exercises. RO3: Investigate simulation and predictive modelling approaches
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. This may be extended to include potential flow theory based modelling as well. Develop deep learning surrogate models for fast prediction of motions, stresses, and loads Validate the deep learning model
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objective is to develop methods that move beyond correlation-based prediction toward causal reasoning, intervention-aware modelling, and interpretable AI systems. This transition from correlation to causation
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3rd August 2026 Languages English English English We are looking for a PhD Candidate in Multi-scale, -physics, -fidelity wind modelling for wind farms Apply for this job See advertisement This is
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of flying birds to predict collision risk. This model will be fed by existing empirical data on bird flight behavioural responses to wind turbines from various bird radar studies. This model will allow
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an aerodynamic bird collision avoidance model, combining computational fluid dynamics (CFD) of the flow around wind turbines with the aerodynamic characteristics of flying birds to predict collision risk. This
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position This PhD project is connected to FME NorthWind (https