Digital Product Owner

Dernière mise à jour: il y a plus de 30 jours

Description de poste

Scientific Fields:

Fault Prognostic, Knowledge Management, Industry 5.0.

Keywords:

Prognostics, Fault Detection, Neuro-Symbolic Methods, Distributed Systems, Data Fusion, Uncertainty Management.

Thesis Subject Summary

As the cutting edge in industrial evolution, Industry 5.0 seamlessly fuses human intelligence with advanced technologies to forge highly personalized and hyper-efficient production systems. Prognostics and health management (PHM) techniques stand at the heart of this transformative era, delivering indispensable tools for proactive maintenance and peak performance optimization. This thesis endeavors to pioneer and validate cutting-edge prognostic models that harness neuro-symbolic methods and data fusion, aligning with the ambitious vision of Industry 5.0. The primary objective is to design a robust and sustainable predictive maintenance solution that not only meets but exceeds optimization and efficiency standards, effectively addressing the myriad challenges inherent in industrial maintenance.

Scientific Challenges

The development of advanced prognostic models for Industry 5.0 involves addressing several scientific challenges:

1. Data and Knowledge Fusion: Develop methods to effectively integrate heterogeneous data from various sources and formalize expert knowledge into a unified framework.
2. Neuro-Symbolic Approaches: Design hybrid models that integrate the deep learning capabilities of neural networks with symbolic systems for knowledge management.
3. Uncertainty Management: Develop techniques to quantify and manage uncertainty in prognostic predictions.
4. Distributed Environments: Design distributed architectures for data processing and the execution of prognostic models.
5. Application to Industry 5.0: Demonstrate the effectiveness of the proposed approaches in real-world use cases within the industry.

Recruitment Modalities:

Application should include:

• Detailed Curriculum Vitae.
• A motivation letter explaining your motivations for pursuing a doctoral thesis.
• MASTER 1 and 2 results.
• Any other documents you consider useful (recommendation letter, etc.).

Skills:

Scientific and Technical Skills:

• Artificial Intelligence: Experience in Machine Learning and Deep Learning.
• Programming: Proficiency in several languages: Object Oriented, Python and AI libraries.
• Modeling, Knowledge representation and Reasoning (rules-based and Expert systems, ontologies).

Interpersonal Skills:

• Being autonomous, having initiative and curiosity.
• Ability to work in a team and have good interpersonal skills.
• Being rigorous.

Thesis Organization:

• Funding: CESI (100%)
• Workplace: Campus CESI Villeurbanne (Lyon) and Campus CESI Nanterre (Paris)
• Start Date: February 2025
• Duration: 3 years