Job Description

Country
France
City
PALAISEAU
Workplace location
PALAISEAU-LE NEXT(FRA)
Employer company
TotalEnergies OneTech
Domain
Research Innovation&Developpt
Type of contract
Internship
Contract duration
6 Months
Experience
Less than 3 years

Context & Environment

The project focuses on AI modeling and degradation prediction, aiming to develop advanced AI based models capable of identifying and quantifying the dominant meteorological stress factors driving photovoltaic degradation, and contributing to the recommendation of indoor testing protocols that most accurately reflect real outdoor conditions. These models will also enable real time estimation of power output and degradation in industrial PV fields equipped with emerging technologies, supporting their integration into MPPT strategies. In addition, the AI models will be embedded into a decision support and field deployment tool to facilitate the industrial rollout of perovskite and tandem modules. The work includes the analysis of both outdoor and indoor datasets, leveraging real PV field measurements to pinpoint key stress factors such as irradiance dose, thermal gradients, day–night cycling, and humidity. Finally, the developed AI/ML frameworks will be applied to the prediction of underlying physical phenomena, using regression methods, time series forecasting, and hybrid models that combine physics based and data driven approaches.

AI Modeling and Degradation Prediction, Develop AI based models to:

• Estimate in real time the power output and degradation of a PV field equipped with new technologies (perovskite/ tandem), for integration into MPPT strategies. Develop AI models capable of predicting degradation under real operating conditions in PV fields, with future integration into MPPT strategies for perovskite and tandem technologies.
• Use large available perovskite degradation datasets for AI and big data analysis.
• Embed these models into a decision support and field deployment tool to facilitate the industrial rollout of perovskite and tandem modules.
• Identify and quantify the dominant meteorological stress factors driving degradation. And contribute to recommend the most representative indoor testing protocols, aligned with real outdoor stressors. Analyze datasets, exploiting real PV field data to identify the main meteorological stress factors (irradiance dose, thermal gradients, day/night cycling, humidity, etc.).
• Apply the developed AI models to the prediction of physical phenomena (regression, time series forecasting, and hybrid models combining physics and data)

Activities

As a AI-Driven Real Time Prediction of Degradation in Emerging Photovoltaic Technologies M/F trainee, your missions will be:

• Predict real time pk degradation in realistic pv field for industrial applications. Extension of current industrial technologies for silicon module fault detection.
• Analyse degradation stress factors using bigdata analysis
• Extending the model to predict additional device parameters such as stability and degradation rates.
• Incorporating process-level metadata (e.g., fabrication conditions) for enhanced robustness.

You will evolve within a team of experienced professionals and with a tutor-coach, the reference for your future profession. Individualized support will help you develop your autonomy and lead you to your diploma!

Candidate Profile

Are you currently enrolled in an engineering school or pursuing a Master’s degree in the field of R&D, and looking for a 6‑month end‑of‑study internship starting in May 2026?

Strong from a first experience in AI/ML applied to time series analysis and the prediction of physical phenomena (regression, time series models, hybrid models combining physics and data), you have knowledge in neural networks? You are familiar with data analysis, modeling, and processing of large datasets? Initial experience in modeling based on artificial intelligence as well as knowledge of Photovoltaic topics will be highly appreciated.

Are you comfortable with office tools and familiar with the Office suite? You will notably use Excel, Word, and PowerPoint.

Are autonomy, rigor, and team spirit an integral part of your qualities?

Do you know how to take initiative?

Strong writing skills as well as professional proficiency in French will be a plus.

If you recognize yourself in the profile we are looking for, don’t hesitate any longer and join the TotalEnergies adventure!

Additional Information

Cette offre concerne un stage conventionné à temps plein, les stages alternés ne sont possibles.

Pour postuler, merci de joindre un CV + lettre de motivation (vos dates de stage doivent être indiquées).

This offer is for a full-time, contractual internship; alternating internships are not possible.

To apply, please attach a CV + cover letter (your internship dates must be indicated).

TotalEnergies values diversity, promotes individual growth and offers equal opportunity careers.

TotalEnergies is a global multi-energy production and supply company: oil and biofuels, natural gas and green gas, renewables and electricity. Its 105,000 employees are committed to making energy ever more affordable, clean, reliable and accessible to as many people as possible. Present in more than 130 countries, TotalEnergies places sustainable development in all its dimensions at the heart of its projects and operations to contribute to the well-being of populations.