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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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, with close interaction with bioinformatics, microbial ecology and experimental crop research at the UU and NOAH partners. You will have access to Utrecht University GPU/HPC infrastructure and large
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Interactions external link groups, with close interaction with bioinformatics, microbial ecology and experimental crop research at the UU and NOAH partners. You will have access to Utrecht University GPU/HPC
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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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agents. Familiarity with responsible AI principles, including fairness, transparency, and data governance. Proven experience with supercomputing / HPC environments. Strong academic writing and