To support the expanding research activities focusing on energy management of building systems, we are looking for an intern student.
The length of the internship (3 to 6 months) may vary depending on the availability of the candidate. Ideally start from September.
The proposed research work is part of a building energy management project at a multi-residential building scale. The goal of the project is to provide the building owner with a tool for water and energy data analysis and FDD (Fault Detection and Diagnosis) functionalities based on the usage of a numerical building energy model and monitoring data (from IoT device) processing. We are looking for an intern student to work on time-series data processing including clustering and forecasting to contribute to the data-driven FDD methodologies. The objective is to use Machine Learning techniques on existing datasets to detect anomalies in the energy systems operation as well as in the building behavior (i.e. water and energy consumption and thermal comfort).
- Master’s student in Computer Science, Information Technology, Mathematics or Engineering
- Good knowledge in Python is a must as well as the ability to work with data warehouse environment (e.g. PostgreSQL)
- Knowledge of Machine learning techniques (e.g. MLPs, CNNs or LSTMs) for time series forecasting is a real plus
- Fluent in French
- Real team player, able to work with autonomy
Interested candidates should send a cover letter, quoting reference number of the offer, and a resume to rh [atw] cenaero [dotv]be