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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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use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and genes underlying responses to altered temperatures and parasite (co-)infection. We
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cell walls, which have been implied in responses to the two parasites. We will also use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and
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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
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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 objective of the research is to use machine learning methods to find models of ship trajectories and traffic patterns that can be used to detect anomalies and predict into the future. The basis for this is
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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