Learning to focus: Physics-Informed Deep Learning for Super-Resolved Ultrasonic Phased-Array Im[...]

Learning to focus: Physics‑Informed Deep Learning for Super‑Resolved Ultrasonic Phased-Array Imaging H/F 1 week ago – Be among the first 25 applicants. Category Category: Mathematics, information, scientific, software Position description Internship aiming to design a physics‑informed deep learning framework for super‑resolved ultrasonic imaging, extending the Total Focusing Method (TFM) beyond its physical and algorithmic limitations. By learning adaptive focusing laws, modeling uncertainties, and incorporating modern architectures like transformers, the project will create interpretable and generalisable imaging models that outperform classical methods in accuracy and speed. This research will contribute to next‑generation ultrasonic inspection systems capable of detecting minute defects in complex materials – enhancing reliability in high‑stakes industrial applications. Contract Internship Contract duration (months) 6 Job title Learning to focus: Physics‑Informed Deep Learning for Super‑Resolved Ultrasonic Phased‑Array Imaging H/F Subject Ultrasonic phased‑array imaging is a core technology in non‑destructive testing (NDT) for detecting defects such as cracks or voids in industrial components. By electronically steering ultrasonic beams, phased arrays generate detailed 3D images of internal structures. The Total Focusing Method (TFM) is the standard reconstruction algorithm, achieving diffraction‑limited resolution by coherently summing signals from all emitter–receiver pairs. However, conventional TFM suffers from key limitations: its resolution is constrained by diffraction and array pitch, grating lobes degrade image quality, and it assumes uniform sound velocity. It also struggles to resolve sub‑wavelength defects, limiting its effectiveness in complex or heterogeneous materials. Recent deep learning methods have improved ultrasonic imaging through denoising and super‑resolution, but most operate as black boxes without physical interpretability. They often fail to generalise across array geometries or material conditions. This internship proposes a physics‑informed deep learning framework that integrates physical modelling of ultrasonic propagation into neural architectures. Instead of static delay‑and‑sum focusing, the approach learns adaptive, reweighted focusing kernels that enhance resolution while maintaining interpretability. The research is structured around six axes: Reweighted TFM: learn per‑pixel focusing weights through supervised or self‑supervised training for adaptive, interpretable imaging. Grating‑lobe analysis: study array pitch effects and compare learned PSFs with theoretical models. Tiny defect imaging: test the method on sub‑wavelength defects using synthetic and experimental data. Coded excitation: train models for artifact‑free imaging under simultaneous transmit‑receive schemes for faster acquisition. Sound speed estimation: incorporate differentiable beamforming to jointly estimate material properties and focus adaptively. Transformer‑based characterization: use multi‑angle scattering data and attention mechanisms for defect classification and interpretation. Expected outcomes include a new interpretable deep model for ultrasonic imaging, quantitative grating‑lobe suppression analysis, and demonstration of sub‑wavelength defect detection. This project bridges data‑driven learning and physical modelling, leading to more robust, adaptive, and explainable ultrasonic imaging systems. The resulting framework could significantly enhance industrial inspection and structural health monitoring by achieving super‑resolution, real‑time imaging of complex materials. Applicant Profile The ideal candidate will have a Master’s degree in Electrical Engineering, Applied Physics, Computer Science, or a related discipline. A strong background in signal and image processing, deep learning (PyTorch, TensorFlow), and programming in Python is expected. Prior experience with acoustic or ultrasonic imaging, inverse problems, or physics‑informed machine learning will be considered a strong advantage. Position location Saclay, France, Ile‑de‑France, Essonne (91). Site: Gif‑sur‑Yvette. Request position start date 01/04/2026 Seniority level Not Applicable Employment type Internship Job function Engineering and Information Technology Industries Research Services #J-18808-Ljbffr