IFPEN is a key player in research on energy and ecological transition (wind power, CCUS, biofuels, geothermal energy, etc.). With regard to wind power in particular, IFPEN has participated in or led several French, European and international research projects over the past decade and is involved in several industrial projects, notably the Provence Grand Large demonstrator. In 2023, IFPEN created the subsidiary GreenWITS, which specialises in digital solutions for the wind energy sector, thus bringing to fruition more than 12 years of research in the field of wind energy.

Main mission:

Develop an innovative methodology to quantify the impact of atmospheric conditions on the actual output of wind farms, by cross-referencing industrial SCADA data and weather reanalyses (ERA-5).

Main activities:

• Data processing: cleaning and preparing large-scale industrial data
• Statistical analysis: classifying atmospheric profiles and analysing production profiles
• Artificial intelligence: exploring architectures for modelling the impact of atmospheric conditions on energy yield

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• Experience with Python (pandas, scikit-learn, TensorFlow/PyTorch)
• Machine learning projects (regression, clustering)
• Knowledge of big data processing
• Basic knowledge of physics/meteorology would be an advantage
• Proficiency in Python for data analysis
• Rigour in processing large amounts of data
• Independence and a taste for research
• Good interpersonal skills and team spirit

Professional English is required. 

Engineering degree or equivalent in the following fields: Mathematics, Computer Science, Data Science, Artificial Intelligence.

The work-study student must enrol in IFPSCHOOL's specialised engineering programme in Offshore Wind Project Development (OWPD), which will enable them to acquire specific skills in offshore wind project development

https://application.ifp-school.com/en/2/candidates/sign_in.

Internship or academic project in data science/machine learning appreciated (3-6 months).