About the Project
Commercially Funded Doctoral Studentship in Collaboration with IMS Group, the University of Bedfordshire, and Luton AI.
A full-time, fully funded PhD studentship for UK Home students is available at the University of Bedfordshire to develop and evaluate responsible artificial intelligence methods for interview assessment and candidate-organisation compatibility.
The studentship is commercially sponsored by IMS Group and will connect doctoral research with a major international workforce-solutions business operating across recruitment, finance, data, marketing and managed IT services.
The successful candidate will also become part of Luton AI, the University of Bedfordshire's applied AI ecosystem. Luton AI brings together academic research, specialist facilities, external partners and real-world projects to support the responsible development and practical application of artificial intelligence.
Through the IMS Group-funded studentship, the successful candidate will receive full tuition-fee support, an annual stipend, academic supervision, access to research infrastructure and specialist facilities, doctoral training, and opportunities to engage with an international commercial partner.
Key dates
- Closing date: Sunday 16 August 2026
- Interview date: Virtual interviews will take place during the week commencing Monday 31 August 2026
- Expected start date: October 2026
- Study mode: Full-time
- Duration: Three years, subject to satisfactory progression
The project
Interviews remain one of the most widely used methods of personnel selection, yet decisions can be influenced by inconsistent judgement, unstructured questioning and the way interviewers interpret verbal and non-verbal behaviour. As recruitment becomes increasingly digital and AI-assisted, there is a pressing need for methods that are demonstrably valid, fair, transparent and acceptable to candidates.
This PhD project will develop and empirically test a Three-Channel Interview Assessment Model combining: verifiable attributes such as qualifications, work samples and assessed skills; self-reported information such as experience, motivations and structured interview responses; and observable interaction signals such as gaze direction, facial movement, posture, gesture and vocal prosody.
The successful candidate will investigate how human interviewers combine these channels, the implicit weight assigned to each source of evidence, and how effects such as halo, similarity bias and cultural interpretation may influence decisions. Controlled experiments and policy-capturing methods may be used to compare interviewer judgements with evidence-based outcome measures.
The project will also explore machine-learning, multimodal data analysis, computer vision, audio analysis and explainable AI methods. Rather than assuming that behavioural signals reveal personality, deception or suitability, the research will test whether any signals provide reliable and incremental information, under what conditions, and with what limitations.
Human, structured and AI-assisted assessment approaches will be compared using appropriate measures of predictive validity, reliability and calibration. Where feasible, the research may include longitudinal validation against outcomes such as performance, progression, retention and candidate experience.
Fairness, privacy, informed consent, accessibility and human oversight will be central to the research design. Protected characteristics will not be used to determine candidate suitability; where demographic information is collected for approved research purposes, it will be used to identify and mitigate differential performance, bias or exclusion.
The longer-term objective is to produce an evidence-based and auditable framework for responsible interview assessment that supports better workforce decisions without replacing professional judgement or reproducing historical inequalities. The research will consider routes to practical evaluation within recruitment and workforce environments relevant to IMS Group.
Person specification
Qualifications
Applicants should normally have:
- A good honours degree, normally at least a UK 2:1 or international equivalent, in computer science, artificial intelligence, data science, human-computer interaction, psychology, organisational psychology, business analytics or a closely related subject.
- A relevant master's degree, or equivalent research or professional experience, would be advantageous.
Applicants with relevant professional or technical experience in recruitment, assessment, HR technology, data analysis or AI who can demonstrate their ability to undertake doctoral-level research may also be considered.
Knowledge
Applicants should demonstrate knowledge of one or more of the following areas:
- Artificial intelligence and machine learning.
- Human-computer interaction, behavioural research or experimental design.
- Computer vision, audio analysis or multimodal data analysis.
- Statistics, psychometrics or quantitative research methods.
- Personnel selection, organisational psychology or person-environment fit.
- Explainable, responsible or human-centred AI.
- Research ethics, fairness, privacy or governance in data-driven systems.
Knowledge of recruitment practice, structured interviews, affective computing or longitudinal research would be beneficial but is not essential.
Experience
Experience in one or more of the following would be advantageous:
- Programming in Python, R or a comparable language.
- Using machine-learning frameworks such as PyTorch, TensorFlow or scikit-learn.
- Working with audio, video, behavioural, assessment or longitudinal data.
- Designing experiments, surveys or quantitative evaluation studies.
- Applying statistical modelling, psychometrics or machine-learning evaluation methods.
- Working in interdisciplinary research or with external organisations.
Supervision
The project will be supervised by:
- Dr Edward Braund, University of Bedfordshire, edward.braund@beds.ac.uk
- Dr Renxi Qiu, University of Bedfordshire, Renxi.Qiu@beds.ac.uk
The project will draw on academic supervisory expertise in organisational psychology, human-computer interaction and responsible AI, alongside input from an IMS Group industry mentor.
Prospective applicants are welcome to contact Dr Braund and Dr Qiu for an informal discussion about the project before submitting an application.
How to apply
Applicants should submit:
- A completed University PhD application through the University's Job portal.
- A current curriculum vitae, including full details of all relevant degrees and qualifications.
- A personal statement explaining their interest in the project, relevant skills and experience, and suitability for doctoral study.
- A short, fully referenced survey of the relevant research field, approximately 750-1,500 words in length, not including references. This should review the validity and limitations of interviews and personnel-selection methods; research on human judgement, bias and structured assessment; and relevant developments in AI-assisted recruitment, multimodal interaction analysis, explainable AI, fairness, privacy and governance. The survey should identify key gaps in the literature and briefly explain how the proposed PhD research could address them. Applicants should use appropriate peer-reviewed academic sources and provide a complete reference list.
- Contact details for two academic or professional referees.
Please note that you may upload a maximum of two files. Each file must be no larger than 2 MB and must be in DOC, DOCX, PDF, RTF or TXT format.
Applications must be submitted by Sunday 16th August 2026.
Application link: [Insert application link]
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