Job Description

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.

The Department of Security and Crime Science currently has around 30 academic staff, from disciplines including psychology, sociology, criminology, geography, political science, economics, mathematics, forensic science, electronic engineering, and computer science. It has established close working relationships with law enforcement agencies, policymakers and businesses to ensure that teaching and research are focused on practical, real-world problems and solutions. In the 2021 Research Excellence Framework (REF) 87% of our submissions were judged to be world-leading or internationally excellent, and all of the department\xe2\x80\x99s case studies were rated as world-leading in terms of their impact on society.

About the role

We are seeking to appoint a Research Fellow to 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 \xe2\x80\x9cIdentifying Data Sources And Developing AI-based Solutions To Analyse Online Fraud\xe2\x80\x9d 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 post is funded to 02/04/2025 in the first instance. The role is offered at UCL Grade 7, Spine Point 29 (\xc2\xa340,524 per annum inclusive of London Allowance).

Please note, 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 \xc2\xa336,832 - \xc2\xa338,466 per annum, inclusive of London Allowance) with payment at Grade 7 being backdated to the date of final submission of the PhD thesis.

Should you have any questions regarding the role, please contact Nilufer Tuptuk (uctzntu@ucl.ac.uk)

If you need reasonable adjustments or a more accessible format to apply for this job online or have any queries about the application process, please contact the Department HR Team (scs.hr@ucl.ac.uk)

A job description and person specification can be accessed at the end of this page.

Security Clearance

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. Potential use cases include alerting and providing summaries of fraud topics to practitioners in crime prevention in real-time, extracting content to help educate the public, classifying fraud data and predicting emerging fraud types.

Candidates in relevant fields such as Artificial Intelligence, especially Natural Language Processing, with an interest in crime and fraud are strongly encouraged to apply.

For a full list of duties and responsibilities, please refer to the , available at the end of this page.

What we offer

As well as the exciting opportunities this role presents we also offer some great benefits some of which are below:

41 Days holiday (including 27 days annual leave 8 bank holiday and 6 closure days, defined benefit career average revalued earnings pension scheme (CARE), cycle to work scheme and season ticket loan, on-Site nursery, on-site gym, enhanced maternity, paternity and adoption pay, employee assistance programme: Staff Support Service and discounted medical insurance,

Our commitment to Equality, Diversity and Inclusion

As London\xe2\x80\x99s Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world\xe2\x80\x99s talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL\xe2\x80\x99s workforce. These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women.

You can read more about our commitment to Equality, Diversity and Inclusion here : https://www.ucl.ac.uk/equality-diversity-inclusion/

Available documents

University College London