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Field
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cutting-edge methods, models and technologies in environmental science, quaternary sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic
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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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experience or knowledge in one or more of the following areas are also a merit: Space physics Plasma physics Computational physics Applied mathematics and modelling Numerical methods for partial differential
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, notifies the excluded candidates by email with delivery receipt, subsequently beginning the application of the selection methods to the admitted candidates. 9. Method(s) and evaluation criteria
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candidates by email with delivery receipt, subsequently beginning the application of the selection methods to the admitted candidates. 9. Method(s) and evaluation criteria: The selection method consists of a
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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
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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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(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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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