Research Assistant - Computer Vision and Machine Learning

The Role and Department

The successful candidate will be based in the Department of Computer Science, Durham University - ranked in the top 10 for Computer Science, Complete University Guide 2025 (Durham University ranked 5^th overall). The Department holds an Athena Swan Silver award, highlighting its commitment to promoting equality across in Science, Engineering and Technology. In the UK REF 2021 research assessment exercise, 97% of our research outputs were classified world-leading or internationally excellent (REF 2021).

The Department of Computer Science hosts well-equipped labs with on-site capabilities comprising vehicle-mounted sensors, drone operations, on/off-road robotics, high-precision geo-localisation, bio-signal data collection, X-ray security scanning, virtual reality and on-demand wide-area surveillance video feeds. In addition Durham University hosts the UK regional supercomputer Bede (128 NVIDIA V100 + 3 NVIDIA Hopper GPUs) in addition to the pan-university Hamilton GPU resource (8 NVIDIA H200 NVL) which both complement our departmental NVIDIA CUDA Compute Cluster (80+ GPUs up to NVIDIA A100) to cater for the increasing GPU compute demands of modern AI-driven research projects.

Applications are invited for a full-time Research Assistant in Computer Vision and Machine Learning (Visual-AI) with a particular emphasis on deep learning for anomaly and out of distribution detection. The post-holder will join Prof. Toby Breckon's research team at Durham University, for an initial fixed-term period of 24 months, funded by an ongoing portfolio of research work primarily spanning aspects of open-world object detection and anomaly/outlier detection for use in both wide-area visual surveillance, aviation security and sensing for future autonomous vehicle/robot sensing (in collaboration with a number of partners).

The successful applicant will be expected to to work on common themes of deep machine learning research with applications across several active research streams within the group. They will consider the use of cutting-edge deep learning algorithms for object detection and tracking, anomaly detection and other generalized data understanding tasks (e.g. behaviour understanding and/or materials discrimination) across a range of imaging modalities. Specifically, they will investigate novel aspects of these tasks, develop software algorithms, and manage their own academic research in addition to co llaboration with a range of external industrial and government collaborators.

The post the offers an outstanding opportunity to gain a strong research track record in an exciting and fast-moving area of applied computer vision and machine learning whilst working in an environment with high levels of external collaboration and industrial research impact.

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