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Field
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large-scale numerical simulations will generate rich datasets describing the relationship between microstructure, deformation mechanisms, and mechanical response. While physics-based simulations involving
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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for magnetic particle transport in fluid flows and validating the results through a laboratory-scale reactor. It combines numerical modeling, AI methods, and hands-on experimental work in the context of advanced
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large-scale numerical simulations will generate rich datasets describing the relationship between microstructure, deformation mechanisms, and mechanical response. While physics-based simulations involving
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environmental geophysics. This PhD project aims to advance the process-based understanding of SSF by combining state-of-the-art geophysical methods with controlled field experiments and numerical modeling
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constructive workplace that offers ample opportunities for personal development. Numerous opportunities for personal growth, illustrated by e.g., conference participation, “doctoral schools” courses
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porous media. Proficiency in scientific programming (Python, C++, or Fortran) is essential. Demonstrated strong analytical and problem-solving skills, with familiarity in numerical methods and an interest
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representation of streamlines inferred from geomagnetic data, from Finlay et al. Nat. Rev. Earth Environ., 4:377–392, 2023 and numerical simulations by Schaeffer et al. Geophys. J. Int., 211(1):1–29, 2017
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knowledge of numerical methods Experience with flying drones and drone data processing (thermal IR, multispectral, LiDAR), fieldwork experience Strong English communication skills. Knowledge
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characterization techniques. The second phase will be dedicated to the development of a Multiphysics numerical model, validated and informed by experimental data, to simulate the WAAM process. Strong