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Field
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supervision of Prof. Yingda Cheng on computational methods and modeling for kinetic equations. The research conducted will involve development of numerical methods, development and analysis of reduced order
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 11 days ago
23 Oct 2025 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile First Stage Researcher (R1
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, and a solid understanding of numerical analysis and familiarity with the use of analytical tools. They should also have knowledge and experience in parallel coding and spectral methods. They must have
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image processing and analysis method development. The position builds on the lab's track-record in the field of computational imaging techniques for super-resolution microscopy and image analysis
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for massively parallel computers. Experience with quantum many-body methods. Preferred Qualifications: A strong computational science background. Familiarity with coupled-cluster method. Understanding
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Positions Country France Application Deadline 15 Oct 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a
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the capture and detection of gases released during SEI formation, particularly CO₂, which can be identified in the form of carbamates. By combining TFBG technology with numerical methods, this project opens new
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for simulating two-phase flows by integrating advanced Artificial Intelligence (AI) techniques with traditional computational fluid dynamics (CFD) methods. The role focuses on transitioning legacy CFD solvers
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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parallel clusters Integrate existing physical models into new software infrastructure for EOS research Benchmark against existing methods and support reproducible, open-science practices Collaborate closely