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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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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.
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: Develop AI-assisted experimental workflows for oxide semiconductor deposition, aiming to accelerate process optimization and unravel new process–property relationships, and contributing to an EU Horizon
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: - A PhD degree in optimization or systems and control theory. - A strong mathematical background and an interest in conducting theoretical research. - A proven track record. - Excellent English
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the effects of optimized gene expression regulation on plant performance under optimal and field-like conditions at the Netherlands Plant Eco-phenotyping Centre. Your research aims to improve tomato Zn and Fe
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not sufficiently understood. In particular, the interplay between surface properties, wafer morphology, and the resulting bonding performance is still largely optimized empirically. In this postdoctoral