We are advancing research at the intersection of ground penetrating radar (GPR) and physics-informed machine learning (PIML) to enable intelligent subsurface sensing and reconstruction for infrastructure monitoring. We are seeking a Machine Learning Engineer Intern to join our research team and contribute to the development of cutting-edge algorithms for automated interpretation of GPR data.
Role Overview
The successful candidate will work on developing and testing advanced machine learning and signal processing techniques for GPR data, with a focus on physics-informed models, inverse problem solving, and data-driven subsurface imaging. The work will involve close collaboration with researchers in geophysics, AI, and computational modelling.
Key Responsibilities
- Develop, implement, and evaluate machine learning algorithms for GPR signal interpretation and imaging.
- Integrate physical principles (e.g., wave propagation, electromagnetics) into learning models.
- Support numerical simulations and forward–inverse modelling using computational tools.
- Collaborate with cross-disciplinary researchers and contribute to scientific publications or technical reports.
Desired Background
Candidates with expertise in one or more of the following areas are highly encouraged to apply:
- Physics-informed machine learning (PIML) or scientific machine learning (SciML)
- Ground Penetrating Radar (GPR) or related electromagnetic sensing
- Forward–inverse problem solving and numerical computation algorithms (e.g., FDTD)
- Educational background in AI, Physics, Geophysics, Electromagnetics, 3D Computer Vision, or Information Engineering
