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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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. The objective of this PhD project is to develop learning-based methods for maritime tracking and prediction in time- and safety-critical applications. Artificial intelligence techniques will be utilized in order
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extended for maritime design challenges. Research areas will cover some of the following themes: Generative methods for design documentation, including automated creation of tender documents, specifications
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engines, and reinforcement learning—can be adapted and extended for maritime design challenges. Research areas will cover some of the following themes: Generative methods for design documentation, including
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10th November 2025 Languages English English English Do you want to develop methods in the interface between AI and forest ecosystems? PhD scholarship: methods for characterizing forest structure
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state estimation and bias correction under uncertain sensing conditions. The PhD candidate will be supervised by Professor Thor I. Fossen, with Assoc. Professor Erlend M. Lervik Coates as co-supervisor
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, data-driven observers that enable physically grounded perception and control for robust state estimation and bias correction under uncertain sensing conditions. The PhD candidate will be supervised by
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work on developing improved methods using deep learning with 3D point clouds to characterize forest ecosystem structure and function. 3D point clouds from LiDAR and photogrammetry provide an excellent
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– unified autonomy architectures, foundation models and robot world models. Focus on implementable methods for onboard robotic autonomy. Experimental deployments and field evaluation of the conducted research
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. Duties of the position Fundamental contributions in foundation models for underwater robotics. Focus on implementable methods for onboard robotic autonomy. Experimental deployments and field evaluation