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Description Ice and mixed-phase clouds remain a significant challenge for both observational methods and atmospheric modeling. However, recent advances in both areas offer promising opportunities for progress
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Ice and mixed-phase clouds remain a significant challenge for both observational methods and atmospheric modeling. However, recent advances in both areas offer promising opportunities for progress
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employment decision is made Strong background in computational modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working
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, components, overall system performance) Numerical methods and simulation tools (e.g., Python/Matlab, CFD modelling, optimization) Beyond technical skills, we value people who contribute to a healthy and
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invite applications for a postdoctoral position in the group of Axel Ringh at the Division of Applied Mathematics and Statistics, Department of Mathematical Sciences, with focus on computational methods
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ranging from transport and production systems to communication solutions and biomedical engineering. The Biomedical Electromagnetic group focuses on developing new, more effective medical methods and
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engineers, and researchers in materials science and nanotechnology. We are developing the superconducting quantum devices, control circuits, firmware, and methods required to make the quantum computer a
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Do you want to contribute to groundbreaking research in the development of a theoretical framework and numerical algorithms for evolving stochastic manifolds? This is an exciting opportunity for a
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that combine first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification methods for multiphase flow models Integrate