Uncertainty quantification in high-dimensional spaces with Bayesian deep learning
Stipend: £16,000 per annum.
Fees: Studentship Fees are covered at UK /EU rates.
The studentship is open to both UK/EU students (full award – tuition plus stipend) and international students (partial award - tuition at the UK/EU rate plus stipend). International applicants are encouraged to apply for a fee scholarship to reduce tuition fees to the UK/EU rate.
The Nature Inspired Computing and Engineering (NICE) Research Group in the Department of Computer Science at the University of Surrey invites applications for a funded studentship in Machine Learning. This PhD position will focus on foundational machine learning topics motivated by applications that aim to improve human life and environment. The successful applicant will be supervised by Dr Yunpeng Li and co-supervised by Dr André Grüning, on the topic of Bayesian deep learning and high-dimensional statistical inference.
The research topic aims to improve the ability of deep neural networks to quantify uncertainty in their predictions, which can benefit vast data science domains, from disease diagnostics to autonomous vehicles. Our work on Monte Carlo sampling and Bayesian classifier fusion has found applications in microwave breast cancer detection, device-free people tracking for smart home, and malaria-vectoring mosquito detection using low-cost mobile phones. Our research is impact-driven and received media coverage from MIT Technology Review, The Guardian, BBC, etc.
A Bachelor’s degree in computer science, engineering, statistics, mathematics, physics or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university).
This studentship is available to UK, EU and overseas students.
IELTS Academic: 6.5 or above (or equivalent) with 6.0 in each individual category.
How to apply
You can apply for this studentship by applying for the Computer Science PhD. You must mention this studentship in your application to be considered.
Please prepare to submit the following documents:
- Your CV
- Degree certificates and transcripts
- Names of two referees (ideally uploading two references at the time of application
- Cover letter explaining your interests, and research proposal (including examples of previous project work).