22 postdoctoral-coastal-modelling PhD positions at NTNU Norwegian University of Science and Technology in Norway
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application process here. About the position The Department of Materials Science and Engineering (IMA) at the Natural Science Faculty, has a vacancy for a position as PhD candidate related to modelling
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-impact career paths in research and higher education, within academia, research institutes, or industry. We will employ a PhD candidate to perform research on development of an AI model that “understands
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marine technology, together with more than 60 PhD students from all over the world. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning
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regulations that provides both incentives and constraints for the maritime energy transition and emission reduction. The research objective of the PhD is to develop models that capture the interaction between
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predictable, complementary renewable source, particularly relevant for coastal nations like Norway. However, the hydrodynamic environment is complex: non-uniform inflows, wave–current interactions, and limited
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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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technology and the equipment industry, fisheries and aquaculture. We also have a strong commitment to the development of sustainable solutions for offshore renewable energy, coastal infrastructure, and marine
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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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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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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