|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|
Brain tumours are cancer of unmet need and are the most common cause of cancer death in under the 40s. Glioblastoma is the most common primary adult brain tumour and carries one of the worst prognoses amongst human cancers, with a median survival time of about 15 months.
The fundamental question to be answered in this project is: can deep learning be used for early detection and prediction of glioblastoma recurrence through imaging? To this end, we will extend the state-of-the-art using Bayesian recurrent variational auto-encoders (VAE) that will be conditioned on the patient meta-data.
An LSTM-RNN will be trained to approximate the predictive distribution of the next set of MR images, given current images and patient meta-data. We will devise an end-to-end training mechanism that will jointly learn the encoding-decoding maps along with the predictions of the spatiotemporal maps.
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 early detection of cancer recurrence in patients with glioblastoma through imaging' 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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