£16,062 stipend, £2,000 RTSG, plus full fees. 

You will receive a tax free stipend at a standard rate of £16,062 per year for 3.5 years. In addition, you will receive a Research Training Support Grant of £2,000 for the 3.5-year duration and your fees will be waived for 3.5 years up to the overseas rate. 

In the real world, decisions are typically made under a degree of uncertainty; we rarely have access to perfect information or complete knowledge about a problem. Humans learn to be more cautious under ambiguous information, but in many cases, we struggle to teach machines to behave in the same way. Machine learning (ML) systems are now ubiquitous in society and are becoming ever more responsible for decisions that impact human safety. Examples of ML making life critical decisions include medical imaging diagnosis systems and self-driving cars. To ensure the safety of these decisions, it is essential that these models can be imbued with a meaningful understanding of uncertainty. 

This research project will advance our understanding of how to effectively and efficiently produce and make use of uncertain predictions from ML models for computer vision and medical image analysis tasks.  We will develop state of the art methods for creating novel probabilistic ML models, which are capable of accurately characterising the inherent uncertainty in the problems they are tasked with. We will also investigate how this information can best be communicated to humans and incorporated into subsequent processes. 

The specific application areas for this projectare flexible and will depend on the student’s interest. Some potential topics include:magnetic resonance image analysis, shape analysis, image registration and tracking, segmentation, image editing and depth estimation. 

This is a computational project, which would suit a student with good mathematical and programming skills and a keen interest in probabilistic machine learning and computer vision. Students will be expected to present their work at top-tier computer vision, medical image analysis or machine learning venues such as CVPR, ICCV/ECCV, MICCAI, NeurIPS etc.  

Eligible applicants will have recently received a good Masters qualification and/or a first or high 2:1 undergraduate in a relevant subject or have substantial recent experience working in a related field. 

This project is with Dr Ivor Simpson, a lecturer in the Predictive Analytics Lab within the AI research group at the University of Sussex. Dr Simpson is an active researcher in this field, with recent work on uncertainty modelling that will be presented at CVPR 2022.The University of Sussex is situated on the outskirts of the fantastic south coast city of Brighton and Hove, adjacent to the beautiful South Downs national park.  

Please discuss applications over email directly with Dr Simpson prior to applying. Note that we are not accepting applications for generic machine learning projects, they should be specifically related to the outlined area. 

The stipend is available to:UK / EU / Overseas. 

The fee waiver is available to:UK / EU / Overseas. 

Eligible applicants will have recently received a good Masters qualification and/or a first or high 2:1 undergraduate in a relevant subject or have substantial recent experience working in a related field. 

Apply online for a full time PhD in Informatics using ourstep by step guide (http://www.sussex.ac.uk/study/phd/apply). Here you will also find details of our entry requirements. 

Please clearly state on your application form that you are applying for the Predictive Uncertainty in Computer Vision Scholarship under the supervision of Dr Ivor Simpson.