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. The topic of the doctoral research will on development of efficient and scalable search engines. This entails design of new retrieval models, efficient query processing, and use of modern hardware
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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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when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
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renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system
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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
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learning models for segmenting and interpreting forest point clouds and rebuild them as real-time, incremental estimation systems. The work supports navigation, self-localization, and traversability work
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cookie and refresh page to watch video, or click here to open video) About the position This PhD project is connected to FME NorthWind (https://www.northwindresearch.no/ ), a Norwegian national research
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, but for example: University of Ulm (Germany): Algorithms for wearable data analysis University of Manchester (UK): Mathematical modeling of hormone rhythms Qualifications and personal qualities: We
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to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the
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for identifying concentration, floe size, geometry, and possibly stage of development. The plan is to build models so that radar measurements alone can be used to populate, as far as possible, the Stage