Job

The Postdoctoral Researcher will work on the development and implementation of AI-driven computational methodologies for solving complex scientific and engineering problems. The major research areas include:
Scientific Machine Learning (SciML) and Physics-Informed Neural Networks (PINNs).
Neural Operators, including Fourier Neural Operators (FNO), Deep Operator Networks (DeepONet), and related architectures.
AI and Deep Learning for forward and inverse PDE problems.
Machine Learning-based surrogate modelling, predictive analytics, and scientific optimization.
AI/LLM-assisted scientific computing and automated computational modelling.
Numerical PDEs, Finite Element Methods (FEM), and scientific computing.
Computational Fluid Dynamics (CFD), heat and mass transfer, and multiphysics modelling.
Digital twins, inverse problems, uncertainty quantification, and sensitivity analysis. High-Performance Computing (HPC) and scalable scientific AI algorithms.