The University of Southampton

High-Precision Laser-Based Manufacturing

  • Entry RequirementsA very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). 
  • Closing dateApplications are accepted throughout the year. The start date will typically be late September, but other dates are possible.
  • Funding: For UK students, tuition fees and a stipend at the UKRI rate plus £2,000 ORC enhancement tax-free per annum for up to 3.5 years (totalling around £21,000 for 2024/25, rising annually). EU and Horizon Europe students are eligible for scholarships. CSC students are eligible for fee waivers. Funding for other international applicants is very limited and highly competitive. Overseas students who have secured or are seeking external funding are welcome to apply.
  • Apply online here. 


Deep Learning for Process Control and Predictive Capability for Laser Machining

Supervisory team: Dr Ben MillsJames Grant-Jacob

Advances in lasers now allow the laser-based processing of almost any material. Innovation in this field is now therefore becoming heavily focussed on making existing processing techniques more precise and efficient. 

Neural networks are a computing paradigm inspired by the biological neurons in the human brain. They offer the capability for learning directly from experimental data, and hence can be used to find solutions even when the problem is not understood by a human. Neural networks therefore offer a remarkable solution to the optimisation and control of laser machining, which itself is far from understood. 

The team is combining state-of-the-art neural networks with high-precision femtosecond laser machining, with the objective of achieving repeatable and high-speed fabrication at resolutions well-below the diffraction limit. 

Your PhD will be focussed on the following applications: 1) convolutional neural networks and reinforcement learning for real-time control of laser machining, 2) generative adversarial networks for simulating and predicting laser machining. Neural networks require large amounts of experimental data for training, and hence this PhD will therefore involve a mixture of experimental photonics and femtosecond laser machining, experimental automation, and programming and designing neural networks.


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