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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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numerical results with observations from scanning and transmission electron microscopy provided by the partners of the ANR project IMP3D (https://anr.fr/Projet-ANR-24-CE08-3737 . - Select a discrete
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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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trips, plus an elective course. Examples of elective courses could include aqueous geochemistry, geomorphology, soils, glaciology, GIS, or numerical modeling. We are seeking candidates who can demonstrate
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engineering or related fields. Strong background in fluid mechanics, scientific computing, numerical methods, PDEs, and/or data-driven modeling Interest in interdisciplinary research and open science
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correction, or many-body quantum systems. o Proficiency in numerical simulations (e.g., tensor networks, quantum circuit modeling). · For Experimentalists: o PhD in atomic/molecular/optical (AMO) physics
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Posting Details Student Title Classification Information Quick Link https://chapman.peopleadmin.com/postings/39133 Job Number SE179024 Position Information Department or Unit Name Fowler School
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and with the 2AT team at Institut Pprime to develop resolvent-based modelling tools for turbulent jets in cases where the jet mean flow is three-dimensional. The researcher shall: - Develop a high-order
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to the effective computation of the data and numerical simulation. Where to apply Website https://resurseumane.ase.ro/non-gaussian-self-similar-processes-enhancing-mathe… Requirements Research