The University of Leeds is inviting applications for the Deep Learning for Outcome Prediction after Pelvic Radiotherapy for PhD students in the UK for the 2019/2020 academic session.
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, they use machine learning to predict toxicity and tumour control after pelvic radiotherapy in Cross-sectional data from a population of patients. They will construct a probabilistic statistical atlas describing the spatial patterns of radiosensitivity across the whole population. They will also create patient-specific sensitivity maps to feed into treatment plan optimization. To alleviate the problem of missing outcome classification data, they will machine learning, e.g. semi-supervised models and cycle GANs.
Worth of Award
- Funding will be awarded on a competitive basis.
- A full standard studentship consists of academic fees (£4,327 in Session 2019/20), together with a maintenance grant (£15,009 in Session 2019/20) paid at standard Research Council rates.
- The PhD Project is funded Worldwide (International, UK, and EU)
To be eligible;
- 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 as 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.
- The university 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 they would therefore particularly encourage applications from UK BME candidates.
- All scholarships will be awarded on the basis of merit.
Deadline: Application closes on August 1, 2019.