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Postdoc on Synergies in Turbulent Natural Convection: Bridging Convolutional Neural Networks, Physics- Informed Machine Learning, and High-Performance Computing for improved modeling

Du 1 février 2024 au 1 février 2025

12 (+6) months

Contacts : ;

The mechanical engineering department of the LISN lab invites applications for a one-year postdoctorate position to conduct cutting-edge research at the intersection of turbulent natural convection, convolutional neural networks (CNN), physics-informed machine learning, and high- performance computing (HPC). The successful candidate will work on advancing the field of super- resolution analysis for turbulent fluid flows using innovative approaches based on numerical and experimental ombroscopy techniques.

The research will build upon recent surveys on machine-learning-based super-resolution
reconstruction of turbulent flows. The candidate will explore and develop methods to enhance the
resolution of turbulent flows through the application of CNN-based techniques, physics-informed loss
functions with access to direct numerical simulations databases produced with high-performance
computing technologies on national supercomputers. The goal is to reconstruct instantaneous turbulent
flows and temperature fields with high fidelity, even in scenarios with limited/partial training data and
noisy inputs.