Postgraduate research opportunities Optimisation and control of laser-driven radiation sources using machine learning

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Key facts

  • Opens: Monday 10 January 2022
  • Number of places: 1
  • Duration: 48 months
  • Funding: Home fee, Stipend

Overview

The project aims to optimise and stabilise laser-driven particle and radiation beams produced in intense laser-solid interactions, through the development and demonstration of a new machine learning platform. This new platform will be based on particle in cell simulations of the laser-plasma interaction physics and will be implemented on experiments at several state-of-the-art high power laser facilities.
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Eligibility

Funded PhD Project (UK Students Only).

Applicants are expected to have a first class or upper second class degree in physics (or an appropriate equivalent qualification).

THE Awards 2019: UK University of the Year Winner
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Project Details

Due to their compact nature and unique properties, high power laser-driven particle and radiation sources are an enabling technology with the potential for impact in a wide range of sectors, including medicine, industry and security.  Routine generation of optimised and stable radiation beams is a key challenge for the development and exploitation of these promising sources. High-repetition-rate, high-intensity short-pulse lasers have recently become available and these are accelerating the development of laser-plasma sources by generating experimental data sufficiently fast for ‘live’ statistical analysis and feedback. Moreover, rapid developments in high performance computing are enabling much higher volumes of complementary simulation data, greatly increasing the parameter space that can be explored via modelling. These parallel developments mean that the rate of scientific discovery in many topics in laser-plasma science will soon no longer be defined by laser and computational hardware but by our ability to use them effectively.

Recent advances in machine learning techniques provide an opportunity to address this challenge. This project aims to do that through the development and demonstration of a new machine learning platform, to predict the conditions needed to optimise, stabilise and control particle and radiation generation in laser-solid interactions. This new platform will be based on particle in cell simulations of the laser-plasma interaction physics and will be implemented on high power laser-plasma experiments at the University of Strathclyde and at laser facilities at the Rutherford Appleton Laboratory.

Further information

The University of Strathclyde and the SUPA Graduate School will provide a full programme of training and development. The student will be based primarily at the University of Strathclyde, and will participate in experiments at the SCAPA laboratories and at external high power laser facilities, such as those at the Central Laser Facility, Rutherford Appleton Laboratory.

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Funding details

iCASE funding with DSTL as the industrial partner.

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Supervisors

Professor McKenna

Professor Paul McKenna

Deputy Associate Principal
Physics

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Dr Ross Gray

Senior Research Fellow
Physics

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Number of places: 1

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Physics

Programme: Physics

PhD
full-time
Start date: Oct 2023 - Sep 2024

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Contact us

Potential applicants are encouraged to contact Prof. Paul McKenna (paul.mckenna@strath.ac.uk) or Dr. Ross Gray (ross.gray@strath.ac.uk) for more information.