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data science, Earth system modeling, or a related field. Demonstrated experience applying statistical or machine learning methods to environmental or geospatial datasets. Proficiency in a scientific
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Design/Computer Aided Manufacturing software applications to: Access, view, import, export, measure, manipulate and construct native electronic drawings and solid models as well as generic file formats
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of Machine Learning-based predictive methods. Desirable requirements Familiarity with AI/machine learning techniques for optimisation and surrogate modelling. Experience of working in multi-partner or industry
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. 5. Familiarity with scientific machine learning and data-driven modelling.
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provided to the PhD student to support the development, training, and implementation of machine learning models. Travel and participation costs for project meetings and international scientific conferences
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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watermarks. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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-learning models that connect powder characteristics and process parameters with the properties of the final components. These models will support faster feedstock qualification and enable predictive quality
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mixing. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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and advanced machine learning. The project will integrate measurements from the SWOT satellite mission with Oxford's Global River Topology (GRIT) hydrography to develop verified, uncertainty-aware