- Expertini Resume Scoring: Our Semantic Matching Algorithm evaluates your CV/Résumé before you apply for this job role: M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems.
Urgent! M2 internship (H/F) - Model order reduction and data assimilation for smart monitoring of mechanical systems Job Opening In Paris – Now Hiring CNRS
Informations générales
Intitulé de l'offre : M2 internship (H/F) - Model order reduction and data assimilation for smart monitoring of mechanical systems
Référence : UMR8006-DIMGOU-001
Lieu de travail : PARIS 13
Pays : France
Date de publication : vendredi 10 octobre 2025
Type de contrat : Convention de stage
Durée du contrat : 6 mois
Date d'embauche prévue : 2 février 2026
Quotité de travail : Complet
Niveau de diplôme préparé : BAC+5
BAP : C - Sciences de l'Ingénieur et instrumentation scientifique
Description du poste
Context
Smart monitoring of mechanical systems (., engineering structures, industrial processes) requires accurate numerical models that can be exploited in real time [1].
Reduced Order Modeling (ROM) and Data Assimilation (DA) are key tools for designing efficient monitoring solutions.
In preparation for an upcoming ANR-funded project starting in 2026, we aim to develop a library of models covering various physical phenomena (., damped vibrations, wave propagation, thermal diffusion).
This harmonized library will serve as a reference framework for this upcoming project to:
•Compare different ROM approaches (., POD-Galerkin, LSPG [2], structure-preserving, auto-encoder) and DA techniques (., EKF, 4D-Var, PBDW [3]) for smart monitoring applications;
• Explore physics-informed Artificial Intelligence (AI) approaches adapted to practical constraints of smart monitoring [4].
Internship objectives
The intern will be responsible for:
1.
Developing a collection of numerical test cases representative of key physical behaviors relevant to smart monitoring.
2.
Implementing ROM methodologies in a harmonized and reusable framework.
3.
Evaluating a data assimilation technique under development at the laboratory.
4.
Delivering a documented GitHub library including reproducible scripts.
Opportunity for PhD continuation
This internship is linked to a funded ANR JCJC project, SPARSE-SHM (Sparse structural health monitoring using signature-informed hybrid modeling).
The goal will be to develop an innovative Structural Health Monitoring (SHM) framework capable of operating with a very limited number of sensors.
The core concept relies on signature-informed modeling.
The principle is to extract only essential and robust information about key parameters of interest from measurements.
A proof of concept has been demonstrated for an SHM application [5].
The PhD will involve theoretical developments (formulation of signature-informed ROMs), advanced numerical methods (coupling ROM–data assimilation–AI), and experimental validation (SHM demonstrators).
References
[1] Chinesta, F., Cueto, E., Abisset-Chavanne, E., Duval, J.
L., & Khaldi, F.
E.
(2018).
Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data.
(No. ART-2018-109564).
[2] Carlberg, K., Farhat, C., Cortial, J., & Amsallem, D.
(2013).
The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows.
Journal of Computational Physics, 242, 623-647.
[3] Maday, Y., Patera, A.
T., Penn, J.
D., & Yano, M.
(2015).
A parameterized‐background data‐weak approach to variational data assimilation: formulation, analysis, and application to acoustics.
International Journal for Numerical Methods in Engineering, 102(5), 933-965.
[4] Cross, E.
J., Gibson, S.
J., Jones, M.
R., Pitchforth, D.
J., Zhang, S., & Rogers, T.
J.
(2021).
Physics-informed machine learning for structural health monitoring.
Structural health monitoring based on data science techniques (pp.
347-367).
[5] Goutaudier, D., Gendre, D., Kehr-Candille, V., & Ohayon, R.
(2020).
Single-sensor approach for impact localization and force reconstruction by using discriminating vibration modes.
Mechanical Systems and Signal Processing, 138, 106534.
Descriptif du profil recherché
Profile
• Final-year engineering student or Master 2 student in computational mechanics, applied mathematics, or scientific computing.
• Interest in discovering research and potentially pursuing a PhD.
Expected skills
• Solid background in numerical methods (PDEs, finite elements, scientific computing).
• Interest in modeling, model order reduction, and data assimilation.
• Proficiency in one scientific programming language: MATLAB, Python, or Julia.
Informations complémentaires
Possibility of PhD continuation: yes, if successful internship (ANR funding secured)
✨ Smart • Intelligent • Private • Secure
Practice for Any Interview Q&A (AI Enabled)
Predict interview Q&A (AI Supported)
Mock interview trainer (AI Supported)
Ace behavioral interviews (AI Powered)
Record interview questions (Confidential)
Master your interviews
Track your answers (Confidential)
Schedule your applications (Confidential)
Create perfect cover letters (AI Supported)
Analyze your resume (NLP Supported)
ATS compatibility check (AI Supported)
Optimize your applications (AI Supported)
O*NET Supported
O*NET Supported
O*NET Supported
O*NET Supported
O*NET Supported
European Union Recommended
Institution Recommended
Institution Recommended
Researcher Recommended
IT Savvy Recommended
Trades Recommended
O*NET Supported
Artist Recommended
Researchers Recommended
Create your account
Access your account
Create your professional profile
Preview your profile
Your saved opportunities
Reviews you've given
Companies you follow
Discover employers
O*NET Supported
Common questions answered
Help for job seekers
How matching works
Customized job suggestions
Fast application process
Manage alert settings
Understanding alerts
How we match resumes
Professional branding guide
Increase your visibility
Get verified status
Learn about our AI
How ATS ranks you
AI-powered matching
Join thousands of professionals who've advanced their careers with our platform
Unlock Your M2 internship Potential: Insight & Career Growth Guide
Real-time M2 internship Jobs Trends in Paris, France (Graphical Representation)
Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph below. This graph displays the job market trends for M2 internship in Paris, France using a bar chart to represent the number of jobs available and a trend line to illustrate the trend over time. Specifically, the graph shows 806 jobs in France and 449 jobs in Paris. This comprehensive analysis highlights market share and opportunities for professionals in M2 internship roles. These dynamic trends provide a better understanding of the job market landscape in these regions.
Great news! CNRS is currently hiring and seeking a M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems to join their team. Feel free to download the job details.
Wait no longer! Are you also interested in exploring similar jobs? Search now: M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems Jobs Paris.
An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at CNRS adheres to the cultural norms as outlined by Expertini.
The fundamental ethical values are:The average salary range for a M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems Jobs France varies, but the pay scale is rated "Standard" in Paris. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.
Key qualifications for M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems typically include Physical Scientists and a list of qualifications and expertise as mentioned in the job specification. Be sure to check the specific job listing for detailed requirements and qualifications.
To improve your chances of getting hired for M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems, consider enhancing your skills. Check your CV/Résumé Score with our free Resume Scoring Tool. We have an in-built Resume Scoring tool that gives you the matching score for each job based on your CV/Résumé once it is uploaded. This can help you align your CV/Résumé according to the job requirements and enhance your skills if needed.
Here are some tips to help you prepare for and ace your job interview:
Before the Interview:To prepare for your M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems interview at CNRS, research the company, understand the job requirements, and practice common interview questions.
Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the CNRS's products or services and be prepared to discuss how you can contribute to their success.
By following these tips, you can increase your chances of making a positive impression and landing the job!
Setting up job alerts for M2 internship (H/F) Model order reduction and data assimilation for smart monitoring of mechanical systems is easy with France Jobs Expertini. Simply visit our job alerts page here, enter your preferred job title and location, and choose how often you want to receive notifications. You'll get the latest job openings sent directly to your email for FREE!