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, and energy systems, including applications in indoor environmental quality, phase‑change materials, and hydrogen injection in natural gas networks, supported by advanced CFD modelling and experimental
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to capture experimentally due to the small scales and opacity of the matrix. Consequently, computational fluid dynamics (CFD) is the best approach for improving these burners. This PhD aims to investigate NH3
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foundation in fluid mechanics and heat transfer Experience with numerical methods (CFD or FVM) Programming skills (e.g. Python, MATLAB, C++, Fortran) Interest in machine learning and data-driven modeling
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exchangers (HX) for optimizing thermal storage in silos. The researcher will deploy conjugate heat transfer CFD models and conduct simulations to provide high-fidelity predictions of available power, charging
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innovative tools combining high-fidelity simulations based on a Lattice Boltzmann Method (LBM) CFD code with advanced data assimilation techniques, notably the Ensemble Kalman Filter (EnKF). By integrating
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computational fluid dynamics (CFD) and computer-aided design (CAD) software. They should also be prepared to engage in both computational analysis and experimental testing as required. Essential criteria
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proficiency in English. Documented experience in CFD and numerical modeling, preferably with OpenFOAM, Nek5000, or LBM. Strong programming skills (C++, Python, or similar). Ability to work independently and
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completed at the deadline for applications, including those resulting from academic degree recognition processes. Preferential Requirements: Proven experience in numerical simulation, namely CFD applied
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degree involving CFD, physics and/or numerical modelling Specific Requirements Programming skills (Python, C++) LanguagesENGLISHLevelExcellent Additional Information Benefits IFP Energies nouvelles is a
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, modelling and simulation in CFD/FEA. Experience with scientific programming (at least Python and C++). Excellent written and spoken English. Very strong team working skills in international, interdisciplinary