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four-year PhD position in Deep Learning for Metocean data (surface waves and ocean parameters) at the Division for Oceanography and Maritime Meteorology at The Norwegian Meteorological Institute (MET
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communication for global ocean environments: Deep learning models perform robustly on certain environments since they are developed by data in low signal-to-noise ratio. To develop more robust models, we will
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, fisheries, aquaculture or the deep sea. The PhD project will focus on how to govern these new models of representation – in other words how to design the ‘rooms’ and which dynamics of the rooms make decisions
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the globe, along with the properties that they carry. However, these currents are profoundly under-sampled by observations and poorly represented in models. One global hotspot of submesoscale ocean
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relevant experience in the development and deployment of machine/deep learning models as well as the use of remote sensing data You must have relevant experience in the development of hydrodynamic and water
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Characterization Techniques Study the advanced electrochemical characterization methods. Gain deep insights into the reaction models associated with PCFCs. 3) Understanding of Electrocatalytic Performance and
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. The position’s field of research Digital twin technology for ocean-going vessels is key to meeting the decarbonization goals set by the International Maritime Organization (IMO). The Advanced Maritime Ship
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exchanged between land, ocean and atmosphere through processes known as global biogeochemical cycles. Research activities in the IMPRS-gBGC aim at a fundamental understanding of these cycles, how