Opportunity to candidate for a Marie-Sklodowska Curie European Postdoctoral Fellowship in AI-based Online Prognostics Model for Remaining Useful Lifetime Estimation of Power Modules

9 Feb 2026
Job Information

Organisation/Company

Université Gustave Eiffel

Research Field

Computer science » Modelling tools
Engineering » Electrical engineering

Researcher Profile

First Stage Researcher (R1)
Recognised Researcher (R2)

Positions

Postdoc Positions

Application Deadline

27 Mar 2026 - 23:59 (Europe/Paris)

Country

France

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

30 Mar 2026

Is the job funded through the EU Research Framework Programme?

Horizon Europe - MSCA

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Université Gustave Eiffel is looking for a candidate to apply for a Postdoctoral Fellowship in the framework of the Marie-Sklodowska Curie Programme 2026.

The Candidate and Université Gustave Eiffel's supervisor will apply together to develop the following research project : AI-based Online Prognostics Model for Remaining Useful Lifetime Estimation of Power Modules

This postdoctoral position is part of a proposal to be submitted for funding under the Marie Skłodowska-Curie Actions (MSCA) European Postdoctoral Fellowship program. The fellowship is contingent upon the selection and approval of the proposal by the European Commission. The successful candidate will work closely with the supervisor to co-develop (from March 30th, 2026 to September 9th, 2026) and submit the funding application, which will include a detailed research plan and a personalized Career Development Plan. 

 If the application is successful, the project will start at the earliest in May 2027. 

1- Motivation and Background
The reliability of power electronic modules remains a key concern for ensuring safe and predictable operation in demanding environments such as transportation and energy conversion. While significant progress has been made in condition monitoring and accelerated lifetime testing, the integration of online prognostic models enabling real-time estimation of the Remaining Useful Lifetime (RUL) is still an open challenge.
This project addresses the need for data-driven and hybrid modeling approaches that combine physics-based knowledge with artificial intelligence (AI) algorithms for accurate, interpretable, and robust health state estimation during field operation.

2- State of the art
The approach generally adopted for estimating the lifetime of a power device is based on accelerated aging tests carried out in the laboratory under standard mission profiles [1], which feed into physics-based models (such as the Coffin-Manson model) [2]. These tests are accelerated in both time and amplitude through a stressing parameter which, in the case of thermomechanical aging tests, is the temperature variation (ΔT). The models developed so far can only handle a single damage mechanism at a time, known a priori, and are unable to accurately extrapolate accelerated results to normal operating conditions. In addition, the results based on lifetime models refer to the time duration until a certain percentage of accumulated failure, which do not provide the time-to-failure of each individual item of interest. These limitations represent major scientific challenges. The modeling approach based on physics-of-failure analysis has greatly contributed to understanding the mechanisms leading to degradation and ultimately to the failure of components [3], [4]. These models quantify the strains induced at the module level during operation, providing a way to describe the damage present in critical areas. They rely on multi-scale finite element simulations with multiphysics couplings between electrical, thermal, and mechanical domains. In addition, it is necessary to consider the continuous evolution of technologies, the regular introduction of new materials, increasingly complex geometries, and the many challenges these pose, particularly for numerical simulation (material properties, geometrical singularities, etc.). All these difficulties, inherent to the study of lifetime and reliability of power modules, make it extremely challenging to obtain accurate lifetime estimations for these devices. Finally, the development of reliable models for robust prognostic tools is essential to reduce the cost of reliability testing, optimize design margin, and to facilitate data-driven preventive maintenance.

3- Objectives
The work initiated in the PhD thesis of M. Ghrabli [1] proposes a new approach to estimating the remaining lifetime of a power module by monitoring the degradation of bonding wires at each load cycle. The methodology is illustrated on Fig. 1. Unlike conventional methods, which are limited to periodic profiles, this approach enables predictions under variable loadings. It combines experimental data, finite element simulations, and probabilistic models. Results depicted on Fig. 1 and Fig. 2 show high extrapolation and interpolation capabilities of the obtained model. Based on these results, the main objective is to develop and validate an online prognostics model capable of estimating the remaining useful lifetime of power modules under varying thermal and electrical stresses.

Specific goals include:
- Development of a hybrid model combining degradation indicators and AI-based algorithms.
- Integration of the model into an online monitoring framework.
- Experimental validation on a dedicated test platform with realistic mission profiles.
To achieve these goals, the project will be carried out through steps detailed bellow:
Step 1 : Validation on real industrial datasets
Test the developed algorithms on additional datasets provided by ECPE industrial partners to assess their robustness, quantify generalization performance, and identify potential limitations.
Step 2: Integration into online monitoring platforms to evaluate real-time feasibility, computational constraints, and monitoring accuracy.
Integrate the models into online monitoring frameworks: at the module level through the SATIE experimental platform, and at the system (converter) level through the facilities at Aalborg University. 
Step 3: Improving extrapolation capabilities
Enhance the models’ extrapolation ability, particularly under low thermal stress conditions, by incorporating physics-informed constraints, synthetic data generation (surrogate model), or hybrid modelling.
[1]M. Ghrabli, M. Bouarroudj, L. Chamoin, and E. Aldea, “Physics-informed Markov chains for remaining useful life prediction of wire bonds in power electronic modules,” Microelectronics Reliability, vol. 167, no. February, p. 115644, 2025, doi: 10.1016/j.microrel.2025.115644

4-Planned secondments

Secondments will be decided with the candidate

5-Planned duration of the project

24 months

Where to apply

E-mail

mounira.bouarroudj@univ-eiffel.fr

Requirements

Research Field

Engineering

Education Level

PhD or equivalent

Skills/Qualifications

Failure analysis and undestanding of failure indicators evolution of poxer converters under power cycling.
Machine learning algorithms and physics of failure modelling

Languages

ENGLISH

Level

Excellent

Languages

FRENCH

Level

Basic

Additional Information
Benefits

MSCA Postdoctoral Fellowships enhance the creative and innovative potential of researchers holding a PhD and who wish to acquire new skills through advanced training, international, interdisciplinary and inter-sectoral mobility. MSCA Postdoctoral Fellowships will be open to excellent researchers of any nationality.

The scheme also encourages researchers to work on research and innovation projects in the non-academic sector and is open to researchers wishing to reintegrate in Europe, to those who are displaced by conflict, as well as to researchers with high potential who are seeking to restart their careers in research.

Fellowships will be provided to excellent researchers, undertaking international mobility either to or between EU Member States or Horizon Europe Associated Countries, as well as to non-associated Third Countries. Applications will be made jointly by the researcher and a beneficiary in the academic or non-academic sector.

Eligibility criteria

Before applying, please make sure you checked the eligibility criteria to apply to the Post-doctoral Fellowship Call 2026: 

Post-doctoral researcher:

For EUROPEAN Fellowship:
- You must commit to submitting only one proposal to the call 2025 with one institution, université Gustave Eiffel
- You must be postdoctoral researchers / have successfully defended your thesis at the date of the MSCA PF call deadline (September 9th, 2026). The successful defence must be unconditional (no further requirements/corrections that need to be addressed) and take place before the call deadline.
- You must have a maximum of 8 years full-time equivalent experience in research (from date of PhD award). Years of experience outside research and career breaks (e.g. due to parental leave), will not count towards the amount of research experience. 
- You must not have resided or carried out their main activity (work, studies, etc.) in FRANCE for more than 12 months in the 36 months immediately before the call deadline.

Selection process

Université Gustave eiffel is looking for a candidate to prepare a joint application to the next Post-doctoral Fellowship call under the MSCA programme (deadline for the MSCA application: September 9th 2026). 

Interested candidate must send their application to université Gustave Eiffel no later than March 27th, 2026. 

The identified candidate will be contacted to prepare the joint application between March 30th until September 9th, 2026. 
Please note that the candidate must secure time to communicate with the supervisor from Université Gustave Eiffel, with partners identified for potential secondment and must secure time to write the project with the support of the supervisor form Université Gsutave Eiffel and the European Project Supporting team. 

Please keep in mind that the post-doctoral position is not secured yet. It will be subject to submitting an application to the European Commission. Should it be accepted, the results will be communicated by March 2027, the Grant agreement signature will be secured in April 2027 and the postdoctoral position can start, at the earliest, from May/June 2027. 

To apply :

1 -  Please download the Application form by clicking here

2 - Fill the Application form

3 - Send the Application form AND a Curriculum Vitae to the supervisors of the research project (Mounira Bouarroudj, mounira.bouarroudj@univ-eiffel.fr ) before March 27th, 2026.

4 - The supervisor will reach you and discuss possible joint application for the incoming MSCA-PF 2026 call.

Additional comments

Please keep in mind that the post-doctoral position is not secured yet. It will be subject to submitting an application to the European Commission. Should it be accepted, the results will be communicated by March 2027, the Grant agreement signature will be secured in April 2027 and the postdoctoral position can start, at the earliest, from May/June 2027. 

Website for additional job details

https://satie.ens-paris-saclay.fr/en

Work Location(s)

Number of offers available

1

Company/Institute

Université Gustave Eiffel - SATIE Laboratory

Country

France

City

Versailles

Postal Code

78000

Street

25 allée des Marronniers

Geofield

Contact

City

Marne-la-Vallée

Website

https://www.univ-eiffel.fr/en/

Street

5 Boulevard Descartes, Champs-sur-Marne

Postal Code

77454

STATUS: EXPIRED

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