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selection criteria You must have a relevant master’s degree in Geotechnical Engineering. Your course of study must correspond to a five-year Norwegian course, where 120 credits have been obtained at master's
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management, utilisation of natural resources, shipping, predictive modelling, or climate risk Core courses in optimisation, microeconomics, scientific methods Elective courses in operations research
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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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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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. 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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will develop models to understand non-equilibrium transport of orbital angular momentum in superconducting hybrid structures. This is part of an effort to determine the merits of superconducting
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of quantum condensed matter physics and has a duration of three years. Your immediate leader will be the Head of Department. Duties of the position The candidate will develop models to understand non
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, including designing, developing and evaluating systems and models to enhance learning through AI technology. A part of this work is also to consider opportunities for innovation related to start-up companies