Looking to list your PhD opportunities? Log in here.
About the Project
Supervisors: Alan Wheelhouse (ASTeC/STFC), James K. Jones (ASTeC/STFC), Valery Dolgashev (SLAC/Stanford Univ.), Roger M. Jones (University of Manchester/Cockcroft Institute)
Project Description:
A studentship is available from October 2023 on the development of a unique machine to deliver FLASH radiotherapy. To achieve this goal we anticipate strong collaboration with STFC’s Daresbury Laboratory and CERN. FLASH-RT entails delivering a high dose over a sub-second timescale and there have been experiments on animals which indicate cancerous regions suffer lethal damage whereas healthy tissues show little impairment. There has also been a recent publication on the first patient receiving such treatment with a dose rate of 15 Gy per 90 ms. FLASH-RT was shown to reproducibly spare normal tissues, while preserving the anti-tumour activity. This marked increase of the differential effect between normal tissues and tumours prompted its clinical translation. To achieve these dose rates conventional machines have been modified –and of course the delivery is far from optimal. We plan to investigate an optimised overall system design to achieve high dose rate within a large tissue field. This work will build on the VHEE machine design and make the substantial modifications necessary for FLASH delivery of radiotherapy. Existing electron machines, albeit at low energies, have already been modified to allow FLASH radiotherapy to be conducted – however this has only been on superficial skin cancers due to the limited energy reach. The student will have the potential to make a major contribution to an exciting and rapidly developing field. This means of delivering radiotherapy has the potential for a new paradigm in the treatment of cancer.
The applicant will be expected to have a first or upper second class degree in physics or other appropriate qualification. Experience in radio frequency accelerators is desirable but not essential, as is experience in accelerator and computational physics. A full graduate programme of training and development is provided by the Cockcroft Institute. The student will be based either at the Cockcroft Institute or at the University of Manchester. It is anticipated that there will be analytical, simulation and experimental aspects to this work. There is the potential for an LTA at SLAC/Stanford to further the practical aspects of this research.
Potential applicants are encouraged to contact:
Prof. R.M. Jones (roger.jones@manchester.ac.uk) for more information. This position will remain open until filled.
Funding and eligibility: Upon acceptance of a student, this project will be funded by the Science and Technology Facilities Council (STFC) for 3.5 years; UK and other EU citizens are eligible to apply. A full package of training and support will be provided by the Cockcroft Institute, and the student will take part in a vibrant accelerator research and education community of over 150 people. An IELTS score of at least 6.5 is required.
Contact for further information: roger.jones@manchester.ac.uk
How to apply: http://www.cockcroft.ac.uk/join-us
Anticipated Start Date: October 2023 for 3.5 Years
Email Now
Why not add a message here
The information you submit to The University of Manchester will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Manchester, United Kingdom
Check out our other PhDs in United Kingdom
Start a New search with our database of over 4,000 PhDs

PhD suggestions
Based on your current search criteria we thought you might be interested in these.
Using a Machine Learning approach to develop a multilingual capable system for collecting and evaluating cyber threat intelligence from online communities.
Kingston University
Machine learning based circuit design and optimisation for image sensors
University of Edinburgh
Making a Computer-Based Tutorial Environment for Mathematics “Intelligent” : The Design, Implementation and Evaluation of a Tutorial System that Learns
Kingston University