I’m Hamza Kamil, a postdoctoral researcher at the Basque Center for Applied Mathematics, Bilbao, Spain. My background is in applied mathematics, and my research interest is the development of efficient numerical algorithms for solving real-world challenges. In particular, I’m interested in scientific machine learning, with two main thrusts:

1. Theory of Scientific ML, where I investigate the mathematical foundations and training dynamics of these algorithms to improve their efficiency and reliability.
2. Physics-Informed Neural Networks (PINNs) for modeling coupled multiphysics processes in real-world environments (e.g., surface–subsurface water and nutrient transport in soils).

Recent Highlights

• “A comprehensive analysis of physics-informed neural networks for solving one-way coupled problems”
  Kamil, H., Soulaïmani, A., Beljadid, A. Computers & Mathematics with Applications, Article 212, 166-189 (2026).
• “Physics-informed neural networks for solving moving interface flow problems using the level set approach”
  Mathieu, M., Kamil, H., Fahsi, A., Soulaïmani, A. Physics of Fluids, Article 107124 (2025).
• “A comparative study of physics-informed neural network strategies for modeling water and nitrogen transport in unsaturated soils”
  Kamil, H., Soulaïmani, A., Beljadid, A. Journal of Hydrology, Article 133624 (2025).