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Field
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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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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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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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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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products. At NTNU, a central part of the project is the development of more predictable and efficient methods for the refactoring and heterologous expression of biosynthetic gene clusters (BGCs
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an increasing human population and counteracting climate change. However, little is known about crop proteomes – the entirety of proteins that execute and control nearly every aspect of life. Therefore, TUM
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refinement. Accurate wind simulation at multiple scales helps in better predicting energy production and reducing operational risks. Some relevant key words (see FME-NorthWind webpage for more details
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on psychological development, mental health, and psychosocial functioning, the project will identify developmental trajectories of attachment and examine factors that may predict these trajectories. The overarching
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class of hydrogels that offer superior control and consistency, aligned to pharmaceutical standards. The potential benefit is to enhance the consistency and predictability of tissue cultures, with
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will feed into an automated stope design workflow, producing variable-length, locally adaptive geometries that optimize the balance between stability, recovery, and dilution control. Through