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Field
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simulation of time dependent non-linear PDEs has emerged as a key technology. A main task of this employment is the development and analysis of numerical methods for wave propagation problems. Particular focus
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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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in the field of theoretical physics • the ability to perform numerical calculations Description: We are looking for one PhD student to work in the Department of Theoretical Physics at the Maria Curie
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international research contexts Your Profile: Excellent Master’s degree in mechanical engineering, energy systems, computational engineering, or a related field Strong background in numerical methods and applied
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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are expected to have a M.Sc. or equivalent in engineering, applied math, physics or similar and a solid background in mechanics and numerical methods. Programming skills (any language) are a plus. If
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if they demonstrate strong relevant skills. Coursework or strong background in computational mechanics / FEM, numerical methods, and scientific programming. Exposure to machine learning / data-driven modelling and/or
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, Computational Science, Physics, Engineering, or a closely related discipline Strong background in differential equations and numerical methods Solid programming skills e.g. Python, C++, Julia or similar Interest
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably