Internship Scalable Local Inversion for Real-Time Ultrasonic Imaging-Saclay-H/F

CEA · Saclay (91) · Stage

Publiée le 21 octobre 2026

Subject

The internship aims to make a physics-informed local inversion method for ultrasonic imaging scalable to extended inspection areas and compatible with real-time operation. The work will address the structure of neighbouring local operators, the stitching of overlapping reconstruction windows, the transferability of learned parameters, and implementation efficiency.

Job description

As an intern at the CEA, you will have the opportunity to work within a world-renowned research environment. Our teams consist of passionate and dedicated experts, providing an ideal setting for learning and collaboration. You will have access to state-of-the-art equipment and top-tier research resources to carry out your assignments. The work conducted may potentially lead to a scientific publication.

Context :

Ultrasonic imaging from Full Matrix Capture data can exceed the resolution of the Total Focusing Method (TFM) when the individual transmitter-receiver contributions are inverted locally.

Local linear direct model

The current physics-informed unrolled method [1] can resolve point reflectors below the Rayleigh limit. The learned inversion parameters can also transfer across nearby depths.

Two point-like reflectors separated by 0.8 mm (left) and 0.4 mm (right). Top: TFM images. Middle: reconstructions obtained at

inference with configuration B. Bottom: lateral profiles at z = 16 mm. Blue crosses and dotted lines indicate the reflector positions.

However, the method currently processes isolated windows and requires the computation of large local operators.

Extending this approach to whole-component inspection requires a scalable strategy that preserves reconstruction quality while meeting real-time constraints.

The objective of the internship is to develop a scalable implementation of local ultrasonic inversion for extended inspection areas. The work will focus on identifying reusable structures across neighbouring operators, combining overlapping local reconstructions consistently, and determining how far locally learned parameters can transfer before retraining becomes necessary.

The internship work will include:

[1] K. Diallo, F. M. Ngolè Mboula and F. Le Bourdais, “Physics-Informed Super-Resolved Ultrasonic Imaging,” 11th Junior Conference on Data Science and Engineering, 2026 [Online].

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Applicant Profile

The internship is intended for a final-year Master’s student or engineering student specialising in applied mathematics, signal processing or scientific computing.

The candidate should have strong Python programming and numerical optimisation skills. Experience with PyTorch, inverse problems, GPU programming or ultrasonic imaging would be an advantage.

Description de l'unité

The Intelligent, Distributed and Embedded Instrumentation Laboratory (LIIDE) develops an integrated hardware and software platform for designing the capabilities of future instrumentation systems. The laboratory focuses jointly on:

  1. Hardware solutions consisting of versatile, modular electronic boards and the software required to operate them, supporting a broad range of sensor technologies.
  2. Innovative artificial intelligence capabilities for distributed measurement and resource-efficient, distributed learning.

Compétences

Voir l'offre d'origine et postuler · Source : site carrières de CEA

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