|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||£15,009 maintenance and cost of fees on a competitive basis|
|Placed On:||23rd May 2019|
|Closes:||1st August 2019|
Modern radiotherapy is highly optimized with respect to individual patient anatomy, utilising 3D anatomical imaging for treatment planning and guidance. This optimization is, however, fundamentally based on underlying assumptions about the relationships between the radiation dose delivered to specific anatomical structures (tumours and normal tissue) and tumour control and/or treatment toxicity – relationships which are still not well understood. Outcome modelling – relating radiation dose to early and long-term patient outcomes – is consequently an extremely active field of research.
In this project, we use machine learning to predict toxicity and tumour control after pelvic radiotherapy in Cross-sectional data from a population of patients. We will construct a probabilistic statistical atlas describing the spatial patterns of radiosensitivity across the whole population. We will also create patient-specific sensitivity maps to feed into treatment plan optimisation. To alleviate the problem of missing outcome classification data, we will machine learning, e.g. semi-supervised models and cycle GANs.
Successful candidates will have an excellent first degree in Engineering, Mathematics, Computer Science, or a related discipline. Candidates are expected to have a solid mathematical background, strong programming skills (in C++/Python/Matlab) and a keen interest in high-impact research work. These will be witnessed by the applicant’s academic transcript and/or GPA. Previous experience in a research environment and a corresponding track record of publishing results in excellent journals and conferences are valued, but not essential.
How to apply
Formal applications for research degree study should be made online through the university's website. Please state clearly in the research information section that the PhD you wish to be considered for is the 'Deep learning for outcome prediction after pelvic radiotherapy' as well a Dr Ali Gooya as your proposed supervisor.
If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.
We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.
If you require any further information please contact the Graduate School Office
e: firstname.lastname@example.org, t: +44 (0)113 343 8000.
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