| Location: | Kingston upon Hull |
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| Salary: | £39,906 to £46,049 |
| Hours: | Full Time |
| Contract Type: | Fixed-Term/Contract |
| Placed On: | 1st May 2026 |
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| Closes: | 28th May 2026 |
| Job Ref: | JR102218 |
The University of Hull is seeking a highly motivated and technically skilled Research Fellow in Artificial Intelligence to join the Centre for Addiction and Mental Health Research (CAMHR). This role offers a unique opportunity to contribute to impactful research that addresses substance use and mental health challenges across diverse communities. CAMHR is funded by the National Institute for Health and Care Research and works in partnership with leading institutions including the University of York and King’s College London, as well as NHS and third-sector organisations.
Serving a population of approximately 1.7 million people across Humber and North Yorkshire, CAMHR focuses on improving outcomes for individuals facing socioeconomic disadvantage, unmet mental health needs, and addiction-related challenges. Its research spans three interconnected areas: young people with substance use and mental health needs, adults experiencing substance use disorders and mental health conditions, and individuals with alcohol-related cognitive impairment.
This position provides significant scope for innovation, acting as a “seed funding” role to develop exploratory, high-impact research ideas and support future grants and trials. The successful candidate will work within a multidisciplinary environment alongside clinicians, statisticians, qualitative researchers, and individuals with lived experience. A strong emphasis is placed on the responsible, ethical, and transparent use of AI to deliver real-world impact.
Key Responsibilities
Applicants should have postgraduate training or equivalent experience in AI, data science, or related disciplines, ideally within healthcare or behavioural science. Strong analytical skills, a collaborative mindset, and a commitment to responsible innovation are essential. This role is ideal for an ambitious researcher eager to apply advanced data-driven methods to improve mental health and addiction outcomes in real-world settings.
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