IPVF – R&D Engineer – AI Modeling for Real-Time Prediction of Degradation in New PV Technologies

contract: COD

Starting date: June 2026

Duration: 24 months

Workplace: IPVF – 18 bd Thomas Gobert, 91120 Palaiseau (France)

Education: PhD or Engineering school

Ref .: PR-CM-2-S

IPVF – Photovoltaic Institute of Île-de-France

IPVF is a scientific and technical pole dedicated to the research and development of solar technologies. It permanently hosts its own staff, as well as the employees of its partners and external companies. IPVF aims to become one of the world's leading centers for research, innovation, and training in the field of energy transition.

IPVF Primary Objective Is To Improve The Performance And Competitiveness Of Photovoltaic Cells And Develop Breakthrough Technologies By Relying On Four Levers

• Ambitious research program.
• The hosting of more than 200 researchers and their laboratories on its Paris-Saclay site.
• A state-of-the-art technology platform (8,000 m²) open to the photovoltaic industry actors, with more than 100 state-of-the-art equipment units located in clean rooms.
• A training program mainly based on a master's degree, the supervision of PhD students, and continuing education.

Perovskite solar cells (PSCs) and associated tandems have gained significant attention due to their high-power conversion efficiency (PCE) and potential for low-cost production. However, their stability under various environmental conditions remains a major challenge. PSCs are susceptible to degradation from factors such as light, heat, humidity, and reverse bias. Addressing these stability issues is crucial for their commercial viability. Machine learning (ML) techniques have been increasingly applied to predict and enhance the stability of PSCs. These methods can analyze large datasets to identify patterns, predict and optimize perovskite degradation.

Perovskite-based photovoltaic technologies and tandem architectures are rapidly reaching maturity and are expected to be deployed industrially in the near term. However, real-time degradation prediction in industrial-scale PV fields remains under active investigation and represents a highly promising industrial challenge. The integration of AI-based models represents a strategic lever to better understand degradation, to predict degradation in real time in PV fields using industrial methods, and to accelerate the validation of these emerging technologies.

MAIN MISSIONS

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-fielddata 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).

Skills & Tools

Knowledge or proficiency in AI/ML approaches applied to time-series analysis and the prediction of physical phenomena (regression, time-series models, hybrid physics-informed + data-driven models).

Strong skills in data analysis, modeling, and processing large datasets.

Experience in AI-based modeling.

Know-How

Critical thinking, scientific rigor, and autonomy.

Ability to work in a collaborative environment (R&D teams, industry partners, laboratories).

Excellent communication and synthesis skills.

CONTACT US

CV and cover letter (with reference PR-CM-2-S) to be sent to:

[email protected] , [email protected] et [email protected]