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)

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!

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).