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at the University of Southampton: https://software-oasis.com/ Our group is developing an innovative class of 3D climate models to explore the atmospheres and climates of Earth and other planets across the Solar
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component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
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climate will warm and recover in a net-zero future. As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques
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background in machine learning, manufacturing, characterization, and testing of novel high performance multifunctional materials. During the project, you will conduct independent research within the designated
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, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques for generating high-resolution climate projections. In addition to developing
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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aptitude for learning new fields of research. The person should have a PhD, or equivalent qualifications and experience, in aerospace engineering, experimental beam physics, or other ion beam uses. It is
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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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emerging multi-omic measurements to build and test machine-learning-based multimodal risk prediction tools. A key element of the role will be to explore how different data types and AI-derived features can
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reflection and learning. The postholder will engage with a range of WorldPop’s external partners and stakeholders across countries, statistical offices, international agencies and Ministerial teams. This role