Machine learning-based synthetic diagnostic and surrogate model of laser-accelerated ions

Key facts

• Opens: Tuesday 28 January 2025
• Deadline: Monday 31 March 2025
• Number of places: 1
• Duration: 42 months
• Funding: Equipment costs, Home fee, Stipend, Travel costs

Eligibility

You must hold a minimum of an upper Second-Class UK Honours degree or international equivalent in a relevant science or engineering discipline.

Project Details

Laser-driven ion sources are compact, highly versatile and capable of producing high-energy ion beams with unique properties. These capabilities make them ideal for potential applications such as cancer therapy and materials analysis. However, achieving consistent and optimised performance remains a significant challenge due to the complex and dynamic nature of laser-plasma interactions. This project seeks to overcome these challenges by developing cutting-edge machine learning (ML) tools to predict and control ion beam properties in real time. Using advanced neural network models trained on experimental and simulation data, the research will enable high-quality ion beam generation and control. These advancements will enable the production of high-quality, stable ion beams tailored for a range of innovative applications.

Key Objectives

• Develop ML-based synthetic diagnostics to predict ion beam properties, such as energy and spectrum, using experimental and simulation data.
• Build a surrogate model for laser-ion acceleration to predict ion beam properties.
• Demonstrate real-time performance during laser-plasma experiments to optimise laser-driven ion beam properties.
• Integrate the ML system into a high power laser facility.

Training and Development

You will receive world-class training in:

You will work closely with leading researchers at the University of Strathclyde, the Central Laser Facility (CLF), and STFC’s Scientific Computing team. Approximately 50% of your time will be based at the University of Strathclyde, with the remaining 50% spent on placement at the CLF. This arrangement will provide you with hands-on experience, advanced training opportunities, and valuable connections with key experts in the field.

• Machine learning and AI: Advanced diagnostic and modelling tools.
• Experimental physics: Hands-on work with high-power laser-plasma systems.
• Computational techniques: High-performance computing (HPC) and advanced simulation tools.

Further information

The project involves a placement at the Central Laser Facility, for ~50% of the duration.

Funding details

The funding package includes full tuition fees and annual stipend, which will increase in line with the UKRI minimum stipend (expected to be £20,780 for 2025/26). Additional support is available for conference attendance, specialised training and placements.

Home Students

To be eligible for a fully funded UK home studentship you must:

UKRI stipend

While there is no funding in place for opportunities marked "unfunded", there are lots of different options to help you fund postgraduate research. Visit funding your postgraduate research for links to government grants, research councils funding and more, that could be available.

• Be a UK national or UK/EU dual national or non-UK national with settled status / pre-settled status / indefinite leave to remain / indefinite leave to enter / discretionary leave / EU migrant worker in the UK or non-UK national with a claim for asylum or the family member of such a person, and
• Have ordinary residence in the UK, Channel Islands, Isle of Man or British Overseas Territory, at the Point of Application, and
• Have three years residency in the UK, Channel Islands, Isle of Man, British Overseas Territory or EEA before the relevant date of application unless residency outside of the UK/ EEA has been of a temporary nature only and of a period less than six years

Professor Paul McKenna

Deputy Associate Principal Physics

Professor Paul Mckenna

Dr Ross Gray (Strathclyde) and Dr Rajeev Pattahil (CLF)

Apply

Number of places: 1

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Contact us

Professor Paul McKenna, University of Strathclyde; paul.mckenna@strath.ac.uk

Research areas