IFPEN is a key player in the energy transition, focusing on ecological and digital solutions.
The internship involves developing intelligent monitoring systems for chemical pilot plants to ensure safe and efficient operations.
Internship Objectives
Conduct a literature review on fault detection techniques, emphasizing machine learning and computer vision.
Implement and adapt computer vision techniques for anomaly detection using synthetic pilot plant data.
Validate the developed methods with real plant data to ensure effectiveness.
Ideal Candidate Profile
Candidates should have an engineering degree or be pursuing a Master's (M2) in Applied Mathematics, AI, Data Science, or Computer Science.
Strong foundation in machine learning and computer vision is required, along with proficiency in Python and experience with deep learning frameworks (TensorFlow/PyTorch).
Familiarity with time series analysis and real-time systems is a plus.
Additional Information
Duration: 4-6 months (February - November 2025)
Location: IFPEN – LYON, accessible by public transport.
Compensation: This is a remunerated internship.
Application Process: Interested candidates should send their CVs and motivation letters to Rayane AMMAR KHODJA with the subject line "Application - Anomaly Detection Computer Vision & FDD Internship 2025".