Responsibilities
- The design of artificial intelligence models dedicated to predictive maintenance, capable of leveraging heterogeneous data sources.
- The identification of strategies for optimizing maintenance planning within a multi-objective framework.
- The validation of the proposed approaches through a real industrial use case, along with their integration into a secure distributed architecture aligned with Industry 4.0 requirements.
- The dissemination of research outcomes through publications in peer-reviewed journals and presentations at international scientific conferences.
Requirements
- Master’s degree (M2) or an engineering degree with specialization in computer science, artificial intelligence, data science, or big data.
- Strong foundations in applied mathematics, statistics, and optimization, with an interest in complex systems modeling.
- Proficiency in major artificial intelligence techniques (machine learning, deep learning).
- Skills in big data processing and heterogeneous data analysis, particularly time series from sensors.
- Good level in scientific programming (Python recommended) and knowledge of data manipulation and analysis tools (pandas, scikit-learn, etc.).
- Awareness of data quality issues (noise, missing data) and their exploitation in complex industrial environments.
- Good level of scientific English, both written and spoken.
- Autonomy, rigor, good organizational skills, initiative, and strong scientific curiosity supporting learning abilities.
- Ability to work in a collaborative academic and industrial environment.
Nice to Have
- Experience with frameworks such as PyTorch, TensorFlow (and possibly federated learning tools).
- Interest in distributed architectures, parallel computing, or real-time data processing.
Work Arrangement
On-site — Pau, France
Additional Information
- Duration: 3 years.
- Starting date: October 2026.
- Selection based on application review and interview.
- Application must include: detailed Curriculum Vitae; motivation letter; academic transcripts and grade reports; recommendation letters if available; any additional supporting documents.
