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Field
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measurements and/or Computational Fluid Dynamics (CFD) simulations. Specifically, you will assess where simplified assumptions in the model fall short. You will then train a machine learning model (such as PySR
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Science, Physics, Mathematics or Computer Science. You have a solid background in computational fluid dynamics (CFD) and be proficient in programming (e.g., Python, Fortran, or C++) and visualization tools
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processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude for team work and excellent communication skills in spoken and written
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measurement techniques and PIV. Familiarity with optics, lasers, image processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude
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postdoctoral position lies at the interface of fluid mechanics, phase-change physics, and industrial application. It is part of ARCNL’s Source Department and the EUV Plasma Processes group. We investigate
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-disciplinary environment? Information Many of the most challenging problems in science and engineering involve physical systems that evolve over time while exhibiting rich mathematical structure. Fluid flows
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engineering involve physical systems that evolve over time while exhibiting rich mathematical structure. Fluid flows, reactive materials, biological systems, plasmas, and complex engineered processes
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Large Eddy Simulations, turbulence research, and the Julia programming language is an advantage but not required. Interest in the intersection of solid/fluid mechanics. Ability to work independently
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design specific constructs to edit transcription factor genes and their target promoters, and analyse the effects of optimized gene expression regulation on plant performance under optimal and field-like
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expertise, preparing them optimally for future challenges.