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extrapolation based on the principle of geometric similarity and the choice of an invariant. Using this method therefore requires precise knowledge of the phenomena involved. As concerns numerical methods, while
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design scalable numerical methods for quantum master equations, implement high-performance simulations, and help build open-source tools for large-scale spin-system modeling. By improving our ability
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involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation. Your Profile: A Masters degree with a strong academic background in physics
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related field Strong background in numerical methods and machine learning Proficiency in at least one programming language (Python, Julia, C++, …) Good analytical skills Good organizational skills and
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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Tensorflow or Pytorch is advantageous Experience in numerical methods for partial differential equations is beneficial Effective communication skills and an interest in contributing to a highly international
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well as theoretical and numerical developments. The project provides opportunities to collaborate with both national and international collaborators, as well as exploring sustainability aspects of the technology. The
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of multi-physics and mixed-dimensional PDEs. The goal of the research is the development and analysis of computational methods combined with model order reduction methods, including data-driven ones
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NASA, Airbus, and Rolls-Royce – to develop cutting-edge, data-enhanced numerical methods that will transform how future aircraft are conceived and certified. You will work within the PinhoLab (led by
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for an additional year. This fellowship will support an emerging scholar whose work uses interdisciplinary methods grounded in Art History to chart new pathways for the field. Area of expertise is open but should