About us
University College London is a world-leading university, currently rated 8th in the QS ratings. The UCL Department of Security and Crime Science undertakes internationally recognised multi-disciplinary research and is dedicated to equipping current and future professionals working in the crime and security field with the skills to meet the challenges of the 21st century. Our teaching programmes focus on developing sophisticated analytical techniques and evidence-based strategies to understand, detect and counter crime and security vulnerabilities. We are committed to bringing disciplines together and having real world impact.
About the role
The Research Fellow will join the Dawes Centre for Future Crime at UCL (Department of Security and Crime Science) to work on a project (funded by the Dawes Trust) that aims to identify data sources and develop AI-based solutions to analyse online fraud.
The project “Identifying Data Sources And Developing AI-based Solutions To Analyse Online Fraud” brings together an interdisciplinary team from Computer Science/AI, Psychology, Security and Crime science to develop AI-based models to investigate online fraud using web and police data. The project will explore the application of Natural Language Processing (NLP) with Machine Learning (ML) including Deep Learning (DL), to build solutions to monitor and predict changing trends and patterns related to online fraud; predict new forms of online fraud, their lifecycle and the modus operandi involved; and use findings to inform approaches to preventing future fraud cases.
This full-time post (36.5 hours per week) is funded to 02/04/2025 in the first instance.
This role is offered at UCL Grade 7, Spine Point 29 (£40,524 per annum inclusive of London Allowance). Appointment at Grade 7 is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Research Assistant Grade 6B (salary £36,832 – £38,466 per annum, inclusive of London Allowance) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.
The project requires the successful candidate to obtain National Security Vetting clearance at the SC level to access police data. To meet National Security Vetting requirements, an applicant would normally have been residing in the UK for a minimum of 5 years.
About you
The post holder will be expected to undertake high-quality research using scientific knowledge and methods from Data Science and Artificial Intelligence (NLP, ML/DL) to develop practical and ethical ways to deliver the objectives of the project including:
- Investigating the quality and use of available data on the web (including social media platforms, online communities and forums) and police data (to be obtained) to train AI-based models to analyse, detect and prevent online fraud.
- Develop AI-based models to classify/categorise fraud types, identify similar frauds (e.g. in terms of modus operandi) and latent topics (i.e. hidden topics within fraud comments, reports and other collected text); analyse temporal patterns in fraud topics using data collected from web data and police data to understand online fraud and the evolution of methods of committing it.
- Developing, prototyping and determining the challenges and benefits, including effectiveness, usability and maintenance, of computational AI-based models for fraud.
Candidates in relevant fields such as Artificial Intelligence, especially Natural Language Processing, with an interest in crime and fraud are strongly encouraged to apply.