Uncertainty quantification in high-dimensional spaces with Bayesian deep learning

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.

Nature Inspired Computing and Engineering (NICE) group