The project will evaluate AI-based behavioural alerting systems using public benchmark video data. It will focus particularly on false alerts, threshold sensitivity, recurring failure modes and the practical burden placed on human reviewers. Working closely with the Principal Investigator, you will contribute to the preparation and documentation of the CUHK Avenue Dataset, support the implementation and testing of selected video anomaly-detection models, and help deliver comparative experiments across different models, thresholds and scene conditions.You will analyse false alerts and recurring failure patterns, maintain accurate and reproducible experimental records, and contribute to figures, tables, technical reports and dissemination materials.

You should hold a degree in Computer Science, Artificial Intelligence, Data Science, Electronic Engineering or a closely related discipline. You will have practical experience of Python for machine learning or data analysis, including experience of implementing, adapting or evaluating machine-learning or deep-learning models. You should also have experience of organising datasets, conducting computational experiments and interpreting quantitative results. Working knowledge of a deep-learning framework such as PyTorch or TensorFlow and relevant scientific Python tools is essential. You should be able to manage your own workload, work collaboratively with the Principal Investigator, and communicate technical findings clearly to a range of audiences.

A postgraduate qualification or equivalent research experience in a relevant field is desirable. Experience in video analytics, anomaly detection, activity recognition would also be advantageous.

For informal enquiries about the role, please contact Dr Ferhat Sadak at f.sadak@aston.ac.uk

What we offer:

• Aston University is a vibrant campus located within Birmingham city centre, which has excellent transport links via bus or train.
• Hybrid working is possible for this role, which means working both on campus and from home depending on the activity. Further information on hybrid working can be found here.
• Excellent training and development opportunities.
• A generous annual leave scheme with 30 days bookable leave and a further 13 days of bank holiday and University closure days.
• Everyday discounts to a number of activities and retailers.
• Great family-friendly policies.
• On-site gym and pool with discounted staff membership available.
• Further information on our Benefits and Rewards can be found here.

This role does not meet the eligibility criteria for new applicants to the Skilled Worker visa route.

See here for information about Skilled Worker eligibility criteria: https://www.gov.uk/skilled-worker-visa

Closing Date: 23 Aug 2026

Unit: Teaching & Research

Salary: £33,951 per annum (pro-rata)