PhD Studentship – Large-scale On-site Manufacturing with Closed-loop 3D Printing and Autonomous Reconstruction

Job Description

Project Overview:

This fully funded 3.5-year PhD studentship is available to UK students and is supported by the Department of Mechanical and Aerospace Engineering at the University of Manchester. The project is aligned with the research theme of Robotics and Smart Manufacturing.

The manufacturing of large-scale products is critical to sectors such as aerospace, renewable energy, and infrastructure, contributing around £100 billion to the UK economy. While additive manufacturing (3D printing) has opened new avenues for product customization and distributed production, current systems are limited by the need for a printing workspace larger than the object being manufactured. This makes the on-site production of large parts difficult, often requiring off-site fabrication and later assembly.

This project aims to overcome that limitation by developing mobile, autonomous 3D printing systems capable of on-site large-scale fabrication. The research will address two core challenges:

Real-time reconstruction and monitoring of large-scale material deposition to enable proactive control during printing.

Error compensation and defect mitigation, addressing the scale-related challenges such as magnified inaccuracies, through adaptive feedback and control mechanisms.

The technologies developed will be validated in diverse applications, including on-site construction, repair, and maintenance tasks.

Funding and Support:

Stipend: £20,780 per year (UKRI rate for 2025/26), tax-free, with annual increases expected.

Tuition Fees: Fully covered.

Eligibility: Open to UK students only.

Candidate Requirements:

Applicants should hold (or expect to obtain) at least a 2:1 UK Honours degree (or equivalent) in:

Mechanical Engineering

Mechatronic Engineering

Manufacturing Engineering

Computer Science

Or a closely related field

Desirable experience includes:

Autonomous systems

Robotics and smart manufacturing

Machine vision

How to Apply:

Interested candidates should contact the project supervisor, Dr Kun Qian, at kun.qian@manchester.ac.uk
.

Your email should include:

A brief introduction and motivation for applying

Details of your academic background and current study status

Any relevant skills or experience related to the project