Efficient numerical methods for Partial Differential Equations

The research position focuses on numerical analysis and mathematical modeling, with particular emphasis on the development and analysis of efficient numerical methods for ordinary and partial differential equations. Research activities may include model order reduction techniques, optimal control methods for dynamical systems, and data-driven approaches for the identification and approximation of mathematical models, including machine learning techniques and Physics-Informed Neural Networks (PINNs). The research will encompass theoretical, algorithmic, and computational aspects, aiming to develop accurate and efficient methodologies for the simulation, analysis, and control of complex dynamical systems.