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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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Geography, and the like Demonstrated analytical, programming, or numerical methods skills (e.g., C++, Fortran, Python, R, Matlab). The candidate must be fluent in written and spoken English and Portuguese
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the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology, including advanced imaging and related analysis Experience in the performance of high pressure
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fields: Material Science, thermal simulation, Metallurgy, Solidification of alloys,... School - Location: Centrale Lille Institute Laboratory: LaMcube Web site: http://lamcube.univ-lille.fr/ Name of
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, including semiconductor heterostructures, graphene, van der Waals materials, and topological insulators; - knowledge of advanced computational, theoretical and numerical methods for studying electronic and
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the combination of Time Spectral Method with Bloch analysis. Integration within the FairCFD Network Within the FairCFD network, you will contribute mainly to WP1, Efficient physics-based numerical methods, and WP2
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"Phase-space-inspired numerical methods for high-frequency wave scattering: from semiclassical analysis through numerical analysis to implementation". The design of fast and reliable algorithms
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for geometric PDEs, in particular for fluid problems posed on surfaces. Both theoretical analysis of numerical methods and code development. Job requirements: PhD in mathematics or applied mathematics, experience
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in numerical methods or scientific computing. Familiarity with machine learning techniques applied to engineering problems is a plus. Good communication skills and ability to work independently and
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complex and performance-oriented features, present a large attack surface that traditional verification techniques—such as formal methods—struggle to cover. To effectively mitigate this growing threat