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Field
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interested in a PhD or postdctoral fellowship in a field related to Computational Fluid Dynamics, please do contact me at b.fraga@bham.ac.uk to prepare your application.
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fluid dynamics and heat transfer to study multiphase flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict multiphase
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combines: Fluid dynamics and heat transfer (theory and experiments), Computational modeling, and Machine learning / computer vision for data analysis and pattern recognition. The goal is to improve
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methods in fluid dynamics and heat transfer to study multiphase flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD project within the framework of the ANR project
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background in Computational Fluid Dynamics, should be familiar with High Performing Computing, including coding in CUDA, and should have knowledge of C++, Julia and Python; d) Ability to work both
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simulation of reactive fluids, computational fluid dynamics) We particularly encourage applications from candidates with a computational background. What we offer Cutting-edge research in a dynamic work
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critical, to ensure expected engine performance is achieved. To predict this complex flow and heat transfer, next-generation Computational Fluid Dynamics (CFD) solvers using Large-Eddy Simulation (LES) and
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field. Research Field: Fluid dynamics, control engineering, or dynamical systems. Required Skills/Qualifications: Experience with experimental methods (laboratory setups, sensors, data acquisition
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systems and control theory. Knowledge of fluid dynamics and related physical modeling. Strong programming skills (e.g. Python, MATLAB, or similar) for data analysis and model development. Ability to work