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estimation, and learning-based prediction models that anticipate the future motion of vessels seen in the radar data, based on the radar data, local geography and historical patterns. The methods
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designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks
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signatures for data association and state estimation, and learning-based prediction models that anticipate the future motion of vessels seen in the radar data, based on the radar data, local geography and
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for the position. Preferred selection criteria Bachelor’s degree in civil engineering Experience in analytical methods and numerical modelling Proficiency in at least one programming language, e.g. C++, Python
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, semantics, functional relationships, and actionable affordances, and enabling predictive reasoning to bridge gaps when observations are missing or unreliable. As an optional extension, a learned world-model
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the broader framework of Embodied AI. The goal is to integrate physical models with deep learning to create interpretable, data-driven observers that enable physically grounded perception and control for robust
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explainable physics-informed RNNs for autonomous navigation and neural observer design within the broader framework of Embodied AI. The goal is to integrate physical models with deep learning to create
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. Developing innovative separation processes is expected to positively impact the circular economy and enable Sustainable Business Model (SBM) innovation. The current project's goal is to contribute
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seasonal emissions such as winter CH4 emissions, using AI tools to develop upscaling tools or upscale to circumpolar region, or using climate modeling such as the Norwegian Earth System Model to constrain
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to ensure that the admission requirements are met, must be uploaded as an attachment. Main tasks Develop machine learning models to produce forest information at local and landscape scales Develop machine