PhD Opportunity – Data-Driven Modeling: A fully funded 3-year PhD position is available in collaboration between Michelin and the LIAS Laboratory at the University of Poitiers, France. The research will focus on the development of innovative, data-driven modeling and control strategies for nonlinear industrial processes in tire manufacturing. The project offers a unique blend of academic and industrial supervision with validation on high-fidelity simulators and real industrial test benches.
🎓 PhD Opportunity – Data-Driven Modeling and Control of Nonlinear Industrial Processes
📊 Summary Table
| Category | Details |
|---|---|
| Title | PhD in Data-Driven Modeling and Control of Nonlinear Industrial Processes |
| Location | Michelin (Clermont-Ferrand) & LIAS Laboratory, University of Poitiers, France |
| Duration | 3 years |
| Funding | Fully funded |
| Supervision | Joint industrial (Michelin) and academic (University of Poitiers) |
| Start Date | As soon as possible |
| Application Deadline | Not specified – apply early for consideration |
🧑🎓 Designation
PhD Researcher
🔬 Research Area
- Data-driven modeling
- Optimal and robust control
- Nonlinear systems
- Tire manufacturing processes
- System identification
- Industrial process automation
📍 Location
- Industry Site: Michelin, Clermont-Ferrand, France
- Academic Site: LIAS Laboratory, University of Poitiers, France
✅ Eligibility / Qualification
- MSc degree from a reputable university
- Strong foundation in:
- Mathematics
- Estimation theory (preferred)
- System identification (preferred)
- Advanced control techniques
- Programming proficiency in Matlab and Python
- Background in mechanics and physics
- Excellent analytical, problem-solving, and communication skills
- Proficiency in English (oral and written)
📝 Job Description
The doctoral research will involve:
- Model Development: Building grey/black-box models to replicate nonlinear industrial dynamics using data-driven methods.
- Control Design: Crafting optimal, robust control laws that reduce time and energy usage while managing uncertainties and sensor noise.
- Validation: Testing models using high-fidelity finite element simulators and real-world industrial setups at Michelin.
The project will address the limitations of classical PDE-based modeling in real-time control and explore hybrid physical-data approaches for improved process performance and environmental efficiency.
📬 How to Apply
Send the following documents by email to both:
📧 guillaume.mercere@univ-poitiers.fr
📧 teddy.virin@michelin.com
Email Subject: Data-Driven Modeling and Control of Nonlinear Industrial Processes
Application must include:
- Academic CV
- Cover letter
- PDF copies of diplomas and academic transcripts
- Certificate of English proficiency
- Any additional supporting documents
⏰ Last Date to Apply
Applications are reviewed on a rolling basis. Apply early for full consideration.
