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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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and philosophers, including one other PhD student (statistics) and two postdocs (spatial forest ecology and philosophy/social science). The candidate is expected to contribute toward developing
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intelligence/machine learning skills. The candidate’s research proposal must be closely connected to the call and the research of NCEI. Excellent skills in written and oral English. Personal suitability and
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fluid dynamics. The successful candidate will be expected to work on all or a subset of the above topics, be proficient in working with large data-sets (observational or numerical), machine learning, and
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language processing or computational linguistics; alternatively, in computer science or machine learning with a specialization in natural language processing Documented knowledge of core machine learning methods and
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Qualifications ? Ph.D. in Physics, Materials Science, or a related field with a concentration in electron microscopy methods ? Experience in the collection and processing of TEM/STEM data ? Computer programming
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. Machine learning algorithms and physics of failure modelling LanguagesENGLISHLevelExcellent LanguagesFRENCHLevelBasic Additional Information Benefits MSCA Postdoctoral Fellowships enhance the creative and
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is necessary have a more critical look at how HPC
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the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target