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capabilities within the underwater domain, and to ensure that a unified modelling approach can operate across robot embodiments – air, ground, and marine. Achieving this requires new simulation tools, scalable
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– and building on recent advances in foundation models, neural model predictive control, and robotic world models – this PhD project will investigate principles and mechanisms for a shared autonomy
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with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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. Experience with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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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 that support the human learning and improving
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the vessels to identify and utilize quiescent periods during harsh weather conditions. To this end data from navigational radars and other relevant sensor channels will be used as input to models that can be
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on optical transmission through falling snow and the models we have are inaccurate. By conducting experiments in Arctic weather over a longer period of time, at different geographic locations, we hope to build
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thermodynamic simulation and modelling activities to support the experimental design, enhance the discovery of reaction mechanisms, underline the governed fundamental phenomena involved in the chemical processing
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involve applications of the existing framework and advancement in the interface towards integrated assessment and energy system models for scenario analysis. The selected candidate will join a team of
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models to enhance learning through AI technology. A part of this work is also to consider opportunities for innovation related to start-up companies. The approach followed encapsulates Design-Based