PhD Studentship: Advancing the Frontier of Robotic Metal Additive Manufacturing through Mechanistic AI

Application deadline: 17/04/2026

This 3.5-year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid.

Robotic metal additive manufacturing offers transformative potential for flexible, large-scale, and high-value metal production. However, its broader industrial adoption is limited by complex process dynamics, limited process understanding, and the lack of reliable control strategies. The PhD will advance its frontier by integrating mechanistic artificial intelligence with robotic additive manufacturing systems to enable intelligent metal processing.

The research will develop physics-informed and data-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning, the project will establish predictive frameworks capable of optimising process parameters, controlling microstructure evolution, and improving part quality in real time. Robotic platforms will be used to implement adaptive manufacturing strategies that respond dynamically to process variations.

The “design-manufacture-inspect-model-test” approach of this project will equip the successful PhD candidate with a wide range of valuable and transferable skills. Ultimately, this research will deliver a new paradigm of intelligent robotic additive manufacturing, contributing to next-generation smart factories and accelerating the development of high-performance and sustainable metal manufacturing technologies.

The PhD studentship is based at The Laser Processing Research Laboratory (LPRL) at The University of Manchester (UoM). The lab sits within the Department of Mechanical and Aerospace Engineering at UoM and performs advanced modelling and technical innovations in the field of laser-based advanced manufacturing and synthesis of new materials. For the robotic additive manufacturing, the lab has a house-developed Laser-Kuka cell with 16kW IPG laser and wire/powder feeding systems. The lead supervisor, Dr Yuze Huang, specialises in laser-matter interactions of metal additive manufacturing, and the co-supervisor, Professor Paul Mativenga (UoM), has extensive expertise in laser materials processing. Dr Chu Lun Alex Leung (Mechanical Engineering at UCL) will also collaborate, he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect that the PhD candidate will also work closely with academic and industrial collaborators and research institutes, including the Photon Science Institute, BP International Centre for Advanced Materials (BP-ICAM), The University of Manchester at Harwell, and the Engineering & Physical Sciences Research Council-funded Henry Royce Institute for Advanced Materials.

The standard academic entry requirement for this PhD is an upper second-class (2:1) honors degree in a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honors degree and a Master’s degree at merit in a discipline directly relevant to the PhD (or international equivalent).

• Prior experience in additive manufacturing, robotic manufacturing, artificial intelligence, machine learning, physics-based modelling, and numerical simulation is desirable. Experience with computational tools such as ANSYS Fluent, STAR-CCM+, or COMSOL Multiphysics would be advantageous.
• Good oral and written communication skills with the ability to prepare presentations, reports and journal papers to the highest levels of quality.
• Good interpersonal skills to work effectively in a team consisting of PhD students and postdoctoral researchers.

To apply, please contact the main supervisor; Dr Yuze Huang, Email: yuze.huang@manchester.ac.uk.Please enclose the following documents:

• A one-page statement addressing your background and suitability for this project.
• A two pages CV

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