Research Grant: AI-Driven Tube Bending Automation

Universidade de Coimbra; Portugal; On-site; EUR 11,000 - 13,000; Full time

Job summary

Universidade de Coimbra invites applications for a research grant in the Department of Civil Engineering. The research focuses on automated tube bending, incorporating advanced automation, computer vision, and non-destructive testing to improve dimensional control and weld-line identification.

Candidates will work with numerical and AI models to predict and compensate for springback, analyze large data sets, and apply machine learning for predictive insights, while performing project management

Qualifications

• Graduates enrolled in a master's degree in relevant engineering fields.
• Non-degree course related to grant activity acceptable.
• Candidates must meet eligibility criteria outlined in RBI-UC RBI-FCT RBI-EBI.

Responsibilities

• Develop automated positioning mechanisms for welded tubes.
• Select and implement dimensional control using computer vision.
• Identify weld lines using non-destructive Eddy current techniques.
• Integrate numerical and AI models to predict and compensate springback.
• Analyze large data sets and apply machine learning for predictive modeling.
• Perform project management to meet established objectives and outputs.

Skills

Computer vision

Data analysis

Machine learning

Project management

Non-destructive testing

AI models

Welding tube alignment

Education

Master's enrollment in Civil/Mechanical/Computer Engineering, Architecture

Bachelor graduates eligible for master's enrolment

Tools

Eddy currents

Job description

Universidade de Coimbra invites applications for a research grant in the Department of Civil Engineering. The research focuses on automated tube bending, incorporating advanced automation, computer vision, and non-destructive testing to improve dimensional control and weld-line identification.

Candidates will work with numerical and AI models to predict and compensate for springback, analyze large data sets, and apply machine learning for predictive insights, while performing project management