Research Fellow - AI for Addiction and Mental Health Research in King's Lynn

The University of Hull is seeking a highly motivated and technically skilled Research Fellow in AI for Addiction and Mental Health Research to join the Centre for Addiction and Mental Health Research (CAMHR). CAMHR is funded through a major National Institute for Health and Care Research (NIHR) award and delivered in partnership with the University of York, King’s College London, and a wide network of NHS and third‐sector organisations. Serving a population of 1.7 million people across Humber and North Yorkshire, the Centre focuses on improving outcomes for communities experiencing significant socioeconomic disadvantage, unmet mental health needs, and substance use challenges.

This is an exciting opportunity for an ambitious researcher to embed advanced AI and data science methods across CAMHR’s applied research programme. CAMHR’s mission is to deliver research that has real-world impact—improving access to effective support and creating practical solutions for addiction and mental health difficulties.

The Centre’s research is framed around three interconnected themes:

• Young people with substance use and mental health presentations
• Adults with substance use (SUD) and mental health presentations (MHP)
• Defining the needs of adults with alcohol‐related cognitive impairment (ARCI)

The Research Fellow will play a cross-cutting and strategic role across all three themes, helping shape a forward‐looking programme of AI-enabled research.

Role Overview

This position provides unique scope for innovation. Acting as a “seed funding” post, the Fellow will develop exploratory, high-impact research ideas, contribute to publications, and help build the foundations for future grants and trials. You will work closely with clinicians, statisticians, qualitative researchers, trial teams, data managers and people with lived experience. A key aspect of the role is ensuring that AI tools are used responsibly, transparently, and in ways that directly support CAMHR’s applied objectives.

Potential areas of contribution include:

• AI-supported evidence synthesis (e.g., automated and LLM-assisted systematic reviews)
• Analysis of large healthcare datasets (primary care, emergency care, routine clinical data)
• Predictive or stratification modelling for prevention and early intervention
• Natural Language Processing of clinical text and qualitative data
• Longitudinal and high-frequency data analysis (e.g. sensors, wearables)
• Methodological work supporting feasibility studies, trials, and service innovation

Key Duties and Responsibilities

Research and Development

• Design, develop, and implement AI and data science approaches that advance CAMHR’s objectives.
• Contribute to the analysis of clinical and healthcare datasets, including coded records, free-text, and longitudinal data.
• Apply machine learning, statistical, and computational methods in collaboration with specialist colleagues.
• Support AI-enabled evidence synthesis, including systematic reviews and policy mapping.
• Produce high-quality research outputs such as publications, conference presentations, and funder reports.

Governance and Ethics

• Ensure all AI activities adhere to ethical, governance, and regulatory requirements for UK clinical research.
• Contribute to ethics submissions, data access requests, and study documentation.

Collaboration and Engagement

• Work with multidisciplinary teams, clinical partners, and people with lived experience.
• Engage with national and international research networks and represent CAMHR at events and conferences.

Funding and Future Development

• Contribute to new grant applications, fellowship bids, and ongoing Centre funding.
• Help shape a coherent AI workplan in the early stages of the appointment.

Teaching and Supervision

• Support the supervision of research assistants, postgraduate students, and early-career researchers where appropriate.

About You

We welcome applicants with experience in AI, machine learning, data science, or computational methods, ideally applied to healthcare, mental health, or behavioural science contexts. This post is ideal for someone with strong technical ability who is excited by interdisciplinary collaboration and real-world impact.

You will:

• Be an experienced researcher with postgraduate training or equivalent professional experience.
• Be confident working independently while collaborating across diverse research teams.
• Bring curiosity, initiative, and a commitment to responsible innovation.

How to Apply

Please submit a CV and covering letter with your application. Your covering letter must address the criteria in the person specification, as shortlisting will be based on how clearly applicants demonstrate these requirements. Applications close at 00:01am of the closing date listed. We reserve the right to close this vacancy early if we receive sufficient applications. If you are interested in this role, please submit your application as early as possible.