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processes. The project will adopt social science and sociotechnical perspectives such as computer supported collaborative work, HCI or organizational sociology to investigate the intra- and
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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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interpretable, 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
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methods in forestry generate vast quantities of data and demand more accurate information. Machine learning allows for the systematization and processing of this data into new forms of information
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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
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learning, statistical estimation methods, software tools, and big-data frameworks. Programming languages such as e.g. Python, C++, and LABVIEW. Emission control rules and regulations in the shipping industry
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contribute to improved animal welfare, reduced antibiotic use, and a more sustainable pig production. The PhD candidate will play a central role in the project, from laboratory method development to field
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to improved animal welfare, reduced antibiotic use, and a more sustainable pig production. The PhD candidate will play a central role in the project, from laboratory method development to field sampling in pig
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intelligence, machine learning, statistical estimation methods, software tools, and big-data frameworks. Programming languages such as e.g. Python, C++, and LABVIEW. Emission control rules and regulations in