PhD Studentship in Machine Learning and Deception Detection

Applications are invited for a University of Warwick PhD Studentship in The Department of Computer Science in collaboration with the Department of Psychology. The PhD will start October 2025 on the broad topic of Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate.

Background: Detecting deception is an important but challenging task for numerous organizations worldwide including in the UK (e.g., Probation Service, Police). Whilst there are different approaches, the most prevalent is polygraph testing which infers deception through the measurement and analysis of physiological responses (e.g., blood pressure, electrodermal activity). However, despite its widespread application, polygraph data capture and analysis has received limited systematic research and does not yet incorporate modern sensors, computing and analytical techniques.

Project: This project aims to explore novel measures (e.g., pupil dilation, remote PPG) and machine learning techniques to enhance existing polygraph data collection and analysis to improve diagnosticity, versatility and reliability. You will integrate, adapt and develop methods (using packages such as BioSPPy) for the collection, processing and analysis of physiological signals measured for determining deception. The project can take different paths depending on the candidate’s skills and interests. As well as receiving technical support and skill development from academic supervisors, you will receive input and direction from UK Government experts to ensure the research is beneficial and relevant to external stakeholders including the Police, Probation Service and National Security.

Requirements:

• Bachelor's degree (minimum 2.1 honours or equivalent) in a STEM subject including Computer Science, Data Science, Engineering, Physics or Maths.
• A relevant MSc/MEng is desirable but strong candidates without postgraduate qualifications will be considered.
• Experience of using machine learning algorithms and toolsets, ideally in a research context.
• Strong programming skills (e.g., Python, Java, C++).
• An interest in physiological signals.
• Home Student (according to the UKRI definition).

Application Process: Please include a short CV and covering letter detailing your motivation and relevant experience for this studentship. For informal enquiries please contact Professor Nathan Griffiths.

Funding Details

An annual UKRI rate stipend for 3.5 years, payment of academic fees, plus a Department RTSG of £3,500 to cover total registration period.

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