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Field
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focus on quantum impurity models (particularly Kondo model). • Strong computational skills (with Python or Julia or C++ or Matlab or equivalent) and using numerical techniques such as exact
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Astrophysics, Space Physics, Aerospace Engineering, Applied Physics, or a closely related field. Previous experience with numerical modelling and simulation relevant to space systems of astrophysics and a
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computational science. Ideal candidates will have strong past experience in numerical analysis and scientific computing. Experiences in scientific machine learning, computational mechanics, and/or nonlocal models
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The candidate will develop numerical procedures in
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theoretical models and numerical tools (master equations, quantum trajectory simulations) to investigate coupling regimes, dynamical phase transitions, and the effects of collective dissipation on coherence and
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to independently design, execute, and interpret numerical simulations, and to integrate modeling results with laboratory, imaging, or field data related to frozen soils or permafrost. Strong scientific writing
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable
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equations (SDEs) are widely used to model time-evolving phenomena in finance, biology, and social dynamics, the underlying data is often highly sensitive. This project tackles the intersection of complex
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implementing numerical models, calibrating them with real measurement data, conducting sensitivity and scalability analyses, and comparing energy community investments with conventional grid reinforcement