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Field
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Postdoctoral Researcher to lead technical service delivery for the Luxembourg Digital Innovation Hub (L-DIH 2.0). Operating at the intersection of process systems engineering, data-driven modeling, and applied
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states are unobserved. Purely data-driven models offer flexibility, but often ignore known biology and provide limited insight into uncertainty and mechanisms. These challenges motivate a broader Biology
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of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest college. We work across disciplines to tackle the challenges of our time through world
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support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning models, high
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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mainly be connected to DRIVE PR, but may also be linked to related projects within data-driven product realisation, digital engineering, systems engineering, geometry assurance, automation, or model-based
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image data. The research explores how AI-driven analysis can move beyond manual reverse-engineering workflows by automating feature extraction and structural interpretation while remaining robust to noise
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
Energy, Transport and Aerospace, funded by the Foundation for Science and Technology (FCT). It focuses on Scientific Artificial Intelligence, optimization, and computational modeling in engineering, aiming
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Researcher and Postdoctoral Researcher Positions in Computational Methods for Civil and Environmental Engineering in the field of data-driven and model-driven simulation methods for atmospheric flows, ocean
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate