PPostdoctoral research in acoustics (CMA END Plus programme)

Euraxess; Le Mans; Hybride; EUR 31 000 - 42 000; Plein temps

Résumé du poste

Le Mans Université LAUM invites applications for a one-year postdoctoral position in acoustics and AI-assisted non-destructive testing and structural health monitoring. The project aims to explore foundation models as explainable scientific assistants in NDT/SHM and to deliver a first proof-of-concept prototype.

The gross monthly salary is €3,300. Start date is 1 October 2026, with planning from 01/10/2026 to 30/09/2027.

Description du poste

Organisation/Company Le Mans Université Department LAUM Research Field Physics » Acoustics Computer science Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 20 Sep 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Research laboratory :

LAUM

Job :

Searcher (The standard job description can be consulted at this link , on pages 1738 et seq., under reference FPRCH007. This document details the main tasks associated with the occupation, as well as the skills, interpersonal abilities and special features that may be required.

Description of the research subject

Towards Intelligent Assistants for The assesment Nondestructive Testing and integrity Monitoring of materials and Structures

Recent advances in foundation models, large language models (LLMs) and generative artificial intelligence are opening new opportunities for nondestructive testing (NDT) and structural health monitoring (SHM). Beyond automating classification tasks, these technologies make it possible to envision intelligent assistants capable of integrating scientific knowledge, interpreting multimodal experimental data, interacting with experts and producing well-reasoned analyses.
This postdoctoral project aims to investigate how these emerging technologies can support NDT and SHM specialists in analysing, interpreting and exploiting inspection data. The research will be carried out at LAUM, a leading research environment in NDT and SHM, particularly in the field of acoustic approaches. The project will build upon the laboratory's experimental facilities, methodologies, datasets and representative case studies while remaining fully open to the latest advances from the
international scientific community.
Rather than developing a new AI model, the objective is to establish a methodology for using foundation models as explainable, robust and physics-consistent scientific assistants. The project will deliver a first proof of concept of an intelligent assistant for NDT and SHM and will lay the scientific foundations for a future PhD project within the END Plus programme

Planning research project

From 01/10/2026 to 30/09/2027

Assigned activities and expected results

Scientific Background

Nondestructive testing and structural health monitoring are undergoing profound changes driven by digital inspection technologies, increasingly sophisticated sensing systems and multimodal data acquisition. Experts are now required to interpret large volumes of heterogeneous information originating from complementary inspection techniques.
At the same time, foundation models and LLMs are transforming scientific data processing. Their ability to combine knowledge, reason over heterogeneous information and interact through natural language offers new perspectives for engineering decision support. However, their application to NDT remains largely unexplored, especially when reliable, explainable and physically consistent interpretations are required.
The project will be conducted within LAUM, whose internationally recognised expertise in NDT and SHM—particularly in acoustic methods—provides an outstanding scientific environment. The research will primarily focus on acoustic techniques, including ultrasonics, nonlinear acoustics, acoustic emission and vibro-acoustics, while remaining open to multimodal inspection strategies whenever scientifically relevant.

Research Objectives

1. Structuring a Scientific Knowledge Base : Organise scientific knowledge, experimental data, publications and engineering expertise into a structured knowledge base suitable for AI-assisted reasoning.
2. Assessing the Contribution of Foundation Models : Evaluate how foundation models and generative AI can support the interpretation of NDT and SHM data, combine heterogeneous observations and assist expert reasoning.
3. Designing an Intelligent Assistant : Develop a first proof-of-concept assistant capable of proposing scientifically grounded interpretations, physically consistent diagnostic hypotheses and preliminary inspection reports.
4. Validation on Representative Case Studies : Assess the proposed methodology using representative applications from the laboratory, including composite materials, bolted assemblies and instrumented structures, together with external datasets whenever appropriate.

Expected Outcomes

The project is expected to provide a better understanding of the role of foundation models in NDT and SHM workflows, a methodology for integrating these technologies into engineering practice, a first intelligent assistant prototype and several high-quality scientific publications. More broadly, it will establish the scientific basis for a future PhD project within the END Plus programme.

Applicants should hold a PhD in acoustics, mechanics, signal processing, instrumentation, computer science or a closely related discipline. Experience in NDT, SHM, data analysis, applied artificial intelligence or experimental research will be appreciated. Previous experience with LLMs is not required, but strong interest in interdisciplinary research combining engineering sciences and AI is expected.

The contract is for a period of one year. The start date is set for 1 October 2026. The gross monthly salary is €3,300.

Eligibility criteria

PhD or equivalent

Selection process

1. Pre-selection ;
2. Shortlisted candidates will be invited to an interview.