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Field
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and with the 2AT team at Institut Pprime to develop a shape-optimisation tool based on resolvent analysis, applied to landing-gear aeroacoustics The researcher will develop a numerical methodology based
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evaluation of modal-decomposition techniques applied to data from high-fidelity numerical simulations of landing-gear aeroacoustics. The researcher will develop and implement modal-decomposition methods using
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located in Fayetteville, a welcoming community ranked as one of the best places to live in the U.S. The growing region surrounding Fayetteville is home to numerous Fortune 500 companies and one
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studies with a very good or excellent final degree and a master's thesis in condensed matter theory or a closely related discipline. Experience with models and analytical or numerical methods/theories
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numerical approach The PhD is part of a Franco-German co-funded project between IFP Energies Nouvelles (IFPEN) at Lyon and the Hamburg University of Technology (TUHH), focusing on the modelling of gas/liquid
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computing environments. Experience with numerical modelling techniques, such as finite difference, finite element, or spectral element methods. Interest in inverse problem formulation and solving and/or
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at understanding the interplay between the above physical effects on the microstructure evolution of Al alloys during additive manufacturing using the phase-field method. The PhD student will use an in-house phase
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(beyond model training) Solid programming skills (Python required; C++/CUDA a plus depending on simulations) Interest in physics-based simulation, numerical methods, or computational engineering Motivation
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application of numerical models Assess uncertainties for future sea-level projections Publish results in international peer-reviewed journals Present findings at national and international conferences
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scarce settings where FM training is not feasible, prompting the exploration of synthetic data generation via direct PDE solvers. This PhD will examine how probabilistic numerical methods can enhance