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Spatially-explicit multi-model ensemble methods for marine ecosystem prediction (C3.5-MPS-Johnson) School of Mathematical and Physical Sciences PhD Research Project Competition Funded Students
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promising results in building prediction models, they are typically data-centric, lack context, and work best for specific feature types. Interpretability is the ability of an ML model to identify the causal
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that integrate structural predictions and neutron scattering data using ensembles instead of current single structure implementations. The integration of simulation and experiment will yield methods that can be
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Doctoral Network CLIMES (Understanding and Predicting Impacts of Climate Extremes under Global Change), funded by the Marie Sklodowska-Curie Actions under the Horizon Europe programme. The CLIMES network
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, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence
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Spatially-explicit multi-model ensemble methods for marine ecosystem prediction (C3.5-MPS-Johnson)
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, such as selective labeling and contrast variation. Our second goal is to improve the accuracy with which solution scattering data of proteins and oligonucleotides can be predicted from the atomic
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Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction
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implemented within an ensemble variational data assimilation system, enabling short-term forecasts based on sea ice concentration and thickness data while providing associated uncertainty estimates. In a second
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable