PhD Studentship: Development of Digital Twin for Smart Sustainable Steel Section Production
Develop a real-time digital twin framework to optimize steel production processes using AI, finite element modeling, and machine learning.
Best for:Best for engineering PhD candidates with expertise in computational modeling, machine learning, and industrial process optimization, seeking hands-on industry collaboration.
Overview
This PhD position at the University of Warwick’s WMG focuses on creating a digital twin for smart steel section production. The project aims to integrate empirical modeling, finite element analysis, and artificial intelligence into a cohesive framework to enable steel producers to predict and optimize production conditions in real time. The successful candidate will work closely with industry partners, leveraging live mill data to reverse-engineer microstructural states and enhance process control. The role includes an industrial placement to ensure practical implementation. Rising energy costs and sustainability demands drive the need for this innovative approach.
What you'll do
- Develop a digital twin framework for steel section production by integrating empirical modeling, finite element analysis, and machine learning.
- Design and implement a real-time data processing system to analyze live mill data (e.g., temperature, rolling loads) for microstructural state prediction.
- Train machine learning models using outputs from computationally intensive finite element simulations to enable rapid process optimization.
- Define the architecture, robustness, and limitations of the combined modeling framework for industrial deployment.
- Collaborate with industry partners to ensure the framework aligns with real-world production constraints and operational needs.
- Conduct an industrial placement to test and refine the digital twin framework in a live production environment.
- Publish findings in academic journals and present research outcomes at conferences.
Requirements
Degree required: A relevant master’s degree (or equivalent) in Engineering, Materials Science, Computer Science, or a related discipline.
Degree offered: PhD
Language: English; proficiency required for academic communication and collaboration.
Instruction: English
- Strong background in computational modeling, finite element analysis, or machine learning.
- Experience with programming languages such as Python, MATLAB, or C++.
- Familiarity with industrial data processing and real-time systems.
- Ability to work collaboratively with industry partners and academic supervisors.
- Passion for sustainable manufacturing and process optimization.
- Open to UK/EU/International applicants (eligibility criteria may apply based on funding scheme).
- No specific age or residency restrictions mentioned.
Funding & terms
How to apply
Deadline: 2027-02-28
Applicants must submit an information request via the University of Warwick’s research degree application portal. Include a CV, academic transcripts, and a research proposal outlining your approach to the project. Shortlisted candidates will be invited for interview.
- CV
- Academic transcripts (BSc + MSc)
- Research proposal (proposing methodology for the digital twin development)
- Two reference letters
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