Postdoc position in Data-Driven Turbulence Modeling

This postdoctoral position in Data-Driven Turbulence Modeling requires advanced expertise in fluid dynamics, specifically turbulence modeling, combined with proficiency in data-driven techniques. Candidates should have a strong background in computational fluid dynamics (CFD) and numerical methods for solving complex flow problems. Experience with machine learning algorithms, statistical analysis, and data assimilation methods applied to turbulence modeling is essential. Proficiency in programming languages such as Python, MATLAB, or C++ is likely important for implementing and testing models. Familiarity with high-performance computing environments and software tools used for simulation and data analysis is advantageous. The role demands a solid understanding of turbulence physics, model development, and validation against experimental or high-fidelity simulation data. Candidates are expected to have a PhD in engineering, physics, applied mathematics, or a related discipline, reflecting rigorous academic training and research experience in fluid mechanics and computational modeling. Strong analytical skills, the ability to work independently and collaboratively within interdisciplinary teams, and effective communication of scientific results are critical. This position is suitable for researchers aiming to advance the state-of-the-art in turbulence modeling by integrating data-driven approaches with traditional physics-based methods to improve predictive capabilities in engineering and environmental applications.