Advances in artificial intelligence have led to a renaissance in learning and extracting patterns from complex data. Despite successes in other areas, applying machine learning techniques in the field of fluid mechanics is relatively new. Most efforts are primarily focused on finding parameters for existing turbulence models. Our lab thus explores big data approaches for geophysical turbulence physics.
We are in search of 3 motivated PhD students to join our group in pursuit of research in one of the following areas: turbulence modeling, artificial intelligence/machine learning, image processing, high performance computing, emerging multiscale methods, numerical analysis, model order reduction & optimization, geophysical fluid dynamics.
– Having an MS degree primarily focused on computational science and engineering.
– Fundamentals: Tensor Calculus, Continuum Mechanics, PDEs, CFD, Finite Difference/Volume, Spectral Methods
– HPC: MPI, openMP, CUDA
– Scientific Programming: Fortran, C/C++, and soft programing such as Python, Matlab
– Packages: TensorFlow, OpenFoam, ParaView
Please note that students must first be admitted into the School of Mechanical and Aerospace Engineering (MAE)’s PhD degree program in order to be eligible to join our lab. Information on MAE graduate program admissions can be found:
We only consider official applications to the Graduate School. However, if interested, please send an email to me for further information.
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