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Field
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, multi-source learning is used to integrate diverse patient populations to build robust models, but having to protect sensitive information. Various modern ML paradigms are proposed to address the diverse
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metocean and biogeochemistry models accounting for climate change impacts across Europe (from the Nordic Seas to the Black Sea and Mediterranean Sea) and the Pan-Arctic region. The PhD project focuses on new
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extrapolating loads along the substructure, for any type of turbine, given specific geometric, inflow and sea-state information. Furthermore, such a machine learning surrogate can speed-up both design and
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maritime and autonomous. You will have a prior background in naval architecture, ocean engineering and mechanical engineering. You should be already familiar with the majority of simulation and experimental
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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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with a wide range of data formats and engaging with data experts and database managers. The second major focus is advanced data analysis and statistical modeling to identify patterns in fish distribution
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this overlooked impact. The turbulent wake governs spreading of pollutants from ships, influence the air-sea gas exchange, and may act as a barrier, affecting connectivity in the marine ecosystem. There is an
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Acknowledgement of Country CSIRO acknowledges the Traditional Owners of the land, sea and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection
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contributes to the improvement of climate prediction models. The Atmospheric Chemistry and Atmospheric Microphysics departments are looking for a committed doctoral student to carry out this project. You can
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simulations of Jakobshavn Isbræ and NEGIS using the open-source Ice Sheet and Sea Level Model (ISSM). The candidate is expected to perform original research that aims at improving our understanding of past