This fully funded PhD position at the University of Edinburgh focuses on investigating how chronic pain and analgesic use influence cognitive decline and dementia. The project, supervised by Dr Chloe Fawns-Ritchie, Prof Riccardo Marioni, Prof Simon Cox, Associate Prof Sara Hägg (Karolinska Institutet), and Prof Barbara Nicholl (University of Glasgow), leverages data from major longitudinal cohorts including UK Biobank, Generation Scotland, and the Lothian Birth Cohort 1936. These datasets provide extensive cognitive, health, sociodemographic, genomic, and neuroimaging data, all linked to electronic health records. The research aims to clarify the pathways connecting chronic pain and pain medication prescribing to cognitive outcomes, using advanced statistical techniques such as Structural Equation Modelling. Key objectives include examining the effects of different analgesic types and prescribing patterns on cognitive function and dementia risk, and exploring how these relationships vary by pain characteristics, genetic risk, and neurostructural factors. The project also allows for the investigation of mediators and moderators such as comorbidities, epigenetic biomarkers, and brain structure. Students will gain expertise in precision medicine, psychology, cognitive and brain ageing, epidemiology, health data science, and complex statistical modelling. Comprehensive training is provided, including opportunities to work with genomic, epigenetic, and neuroimaging data, and to visit collaborating labs such as the Molecular Epidemiology of Ageing lab at Karolinska Institutet. Applicants should apply to this specific project via the University of Edinburgh's EUCLID system, submitting the Precision Medicine Recruitment Form and all requested documents. The deadline for applications is 12 January 2026. This opportunity is ideal for candidates interested in dementia research, chronic pain, advanced modelling, and interdisciplinary health science.

<p>This fully funded PhD opportunity at the University of Edinburgh focuses on the relationship between chronic pain, prescribing practices, and cognitive decline, including dementia. The project is part of the Precision Medicine Doctoral Training Programme at the Usher Institute and is supervised by a multidisciplinary team: Dr Chloe Fawns-Ritchie, Prof Riccardo Marioni, Prof Simon Cox, Associate Prof Sara Hägg (Karolinska Institutet), and Prof Barbara Nicholl (University of Glasgow).</p><p>The research will leverage data from three major longitudinal cohorts (UK Biobank, Generation Scotland, and Lothian Birth Cohort 1936) to investigate how chronic pain and the use of pain-relieving medications contribute to cognitive decline and dementia. The project will use advanced statistical techniques, including Structural Equation Modelling, to explore the influence of different types of analgesics, prescribing patterns, and pain characteristics on cognitive outcomes. It will also examine genetic, neurostructural, and epigenetic factors as potential mediators and moderators.</p><p>Students will gain training in cognitive, brain and biological ageing, epidemiology, health data science, and advanced statistical modelling, with opportunities to analyse electronic health record data and work with genomic and neuroimaging datasets. The student will be based at the Department of Psychology, School of Philosophy, Psychology and Language Sciences, and will collaborate with the Edinburgh Futures Institute and the Institute of Genetics and Cancer. Lab visits to the University of Glasgow and Karolinska Institutet are included.</p><p>The position is open to UK, EU, and international applicants from backgrounds such as psychology, neuroscience, epidemiology, health data science, and biomedical research. Applicants must apply to the specific project, including project details in the research proposal section of the EUCLID application, and upload a CV and other requested documents. The deadline for applications is 12 January 2026. For more information, visit the provided links or attend the Q&A session in December.</p>