PhD Studentship: A Human-Factors Investigation of Automation, Decision-Support and Machine Lear[...] in Nottingham

A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty of Engineering at the University of Nottingham, which conducts cutting-edge research into human–technology interaction, cognitive workload, decision-making, and the design and evaluation of complex socio-technical systems. The project is jointly supervised with the Law and Technology Research Centre in the School of Law, reflecting a strong interdisciplinary focus on responsible and accountable deployment of algorithmic systems.

The research will investigate how clinicians interact with automated and machine learning–based decision-support systems, with a particular focus on cognitive workload, trust, situational awareness, and decision quality. The project will examine how system design, automation characteristics, and regulatory or governance constraints shape human performance and patient outcomes in safety-critical clinical contexts.

Healthcare systems are increasingly adopting algorithmic and AI-driven tools to support clinical decision-making. While these technologies promise improvements in efficiency, consistency and patient outcomes; they also introduce new cognitive demands, risks, and accountability challenges. This project aims to contribute to the development of human-centred, safe, and trustworthy clinical decision-support systems, ensuring that automation enhances rather than undermines clinical expertise and patient safety. The project will adopt a broad, systems-oriented perspective, informed by human factors science and an understanding of the legal and regulatory landscape surrounding clinical AI.

Clinical decision-making is inherently complex, time-pressured, and high-risk. The introduction of algorithmic decision-support systems and machine learning models has the potential to alter cognitive workload, shift responsibility, and reshape how decisions are made and justified. However, there is limited empirical understanding of how clinicians interact with such systems in practice, and how human factors considerations intersect with legal and regulatory requirements such as accountability, transparency, and oversight. This project addresses these challenges by combining experimental human factors methods with interdisciplinary perspectives on law, governance, and technology. The work will generate evidence to inform the design, evaluation, and regulation of clinical AI systems, supporting safer and more effective deployment in real-world healthcare settings.

The successful candidate will develop and conduct empirical research examining human interaction with algorithmic decision-support systems in clinical contexts. The precise focus of the project will be refined collaboratively with the student, but may include experimental studies, simulation-based evaluation, cognitive workload assessment, and qualitative or mixed-methods approaches, with a view to informing design principles, evaluation frameworks, guidance, or policy recommendations. The student will be based in the Human Factors Research Group within the Faculty of Engineering and will work closely with researchers in the Law and Technology Research Centre. Depending on the student’s background, tailored training will be provided in human factors methods or legal and regulatory frameworks relevant to clinical AI, particularly during the first year of the PhD.

The project will also provide opportunities to engage with a wider interdisciplinary research environment at Nottingham, including potential collaboration with groups such as the MindTech within the School of Medicine, the Institute for Policy and Engagement, CHART (Centre for Health, Ageing and Rehabilitation Technologies), and the Responsible AI (RAI) initiative. The student will have access to excellent research facilities, including dedicated human factors and usability laboratories, and advanced immersive technologies, such as the Faculty’s large-scale virtual reality teaching and research facilities. Collectively, these opportunities will support the development of a strong interdisciplinary research profile and prepare the student for careers in academia, industry, policy, or healthcare technology regulation.

We are seeking an enthusiastic, self-motivated, and intellectually curious candidate with a strong interest in interdisciplinary research. Applicants should have, or expect to obtain, a first-class or upper second-class (2:1). A Master’s degree (or one near completion) in a related field is desirable, but not essential for candidates who can demonstrate strong research potential. Suitable backgrounds include, but are not limited to:

• Human Factors
• Law (particularly law and technology, medical law, or data governance)
• Psychology (particularly cognitive or applied psychology)
• Cognitive Science
• Human–Computer Interaction
• Engineering or Computer Science
• Health sciences

Experience in empirical research, experimental design, data analysis, or computational methods is desirable but not essential. Candidates from non–human factors backgrounds will receive structured training in human factors and experimental methods, while candidates from engineering or psychology backgrounds will have opportunities to develop legal and regulatory literacy relevant to algorithmic decision-making in healthcare. Bespoke training opportunities will be offered to students based on their previous academic background and project direction.

Subject to a competitive selection process, the successful candidate will be supported for funding by the University of Nottingham covering the Home tuition fees and UKRI stipend. Funding duration is 42 months.

The University of Nottingham is committed to equality, diversity, and inclusion and welcomes applications from all sections of society. The Faculty of Engineering provides a supportive and vibrant research environment, with access to excellent facilities, a strong postgraduate research community, and extensive training opportunities through the Researcher Academy and Faculty-specific provision. The Faculty of Engineering provides a thriving working environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research Group Reps to enhance the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs, including sessions on paper writing, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners.

Applicants should submit their application to Dr Kyle Harrington at Kyle.Harrington@Nottingham.ac.uk and attach two documents:

• A CV (maximum 2 pages). CVs should include:

• Relevant experience
• Education and grades (listing relevant modules taken, and predicted grades for qualifications in progress)
• Dissertation topic and dissertation mark (if applicable)
• Names of publications or patents (if applicable)
• Academic Prizes, Grants or Awards Received

• A brief research proposal (maximum 1 page of text)
• A 300–500 word statement explaining why they are the ideal candidate for this project

References and any diagrams should be included within the relevant page limits. Applicants should clearly state in the body of their email whether they are applying for the Home or International funding route, based on their fee status. Application deadline: Monday 23rd February, 11:00am GMT. Interviews expected: Week commencing 2nd March. Informal enquiries can be directed to Dr Kyle Harrington at: Kyle.Harrington@Nottingham.ac.uk