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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 18 days ago
models as predictive tools to address questions regarding the response of deep-sea ecosystems to various pressures. A key question addresses the best combination of ML and network analysis to maximize
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the SDM modelling for these five gorgonian species to deeper area in the NW Mediterranean sea. - run BIOMOD2 suite on Blouet et al. (2024) dataset - gather environmental descriptors for extending the SDM
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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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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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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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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