The details of the available projects for the Infection, Immunity & Repair theme are outlined on this page. You can find other projects on the Infection, Immunity & Repair and Neuroscience & Mental Health pages. A full list of our available projects can be downloaded below.
For full project descriptions, including contact details for the lead supervisor, click the download link by the project title.
Applications to the GW4 BioMed MRC DTP will be accepted via this online survey until 5 pm on 23rd November 2020. For guidance on the application criteria and decision timeline, please see the information here.
There are 237 million medication errors in England each year and 28% harm patients. This project will use artificial intelligence (AI) to identify possible causes of medication errors reported in the NHS. The student will gain skills in AI & statistics for large data sets through training, and knowledge of NHS medicines safety via a placement. This project will suit students from a wide variety of backgrounds, e.g. computer science or a health profession.
Lead Supervisor: Dr Matthew Jones
People with neurodevelopmental conditions (NDCs) often score lower on cognitive tests, which is related to poor mental health. However, they may have enhanced mental abilities (‘hidden talents’) due to their symptoms and related adversities. Drawing on the lived experience of people with NDCs and population-based genetic, cognitive, and neuroimaging data, the project will investigate the role of hidden talents in understanding and enhancing mental health in NDCs.
Lead Supervisor: Dr Punit Shah
The effect of immune dysfunction on risk of Alzheimer’s disease is poorly understood. This PhD will use genetic and observational epidemiology to understand if and how immune parameters such as complement proteins, cytokines and antibodies, as well as immune disorders such as rheumatoid arthritis and Multiple Sclerosis alter risk of Alzheimer’s disease and cognitive decline.
Lead Supervisor: Dr Emma Anderson
This PhD is a unique opportunity to work with the Universities of Bristol and Bath and the regional NHS Modelling and Analytics Unit to apply state-of-the-art data science methodologies to develop an analytic framework for the routine evaluation of complex healthcare interventions. This ambitious project will include the development of promising methods and their use for ‘real life’ interventions, in order to facilitate implementation across the NHS.
Lead Supervisor: Dr Frank de Vocht
This PhD, which sits at the interface of medical and social sciences, will use detailed life course data to explore the complex and bidirectional associations of mental health and neurodevelopmental conditions with decisions about participation in higher education – a topic with strong policy relevance. You will gain experience in mental health research, education research, and analysis of longitudinal and genetic data.
Lead Supervisor: Dr Laura Howe
Does drinking moderate amounts of alcohol benefit health – or do only healthy people get to drink moderately? This is just one question solved in epidemiology with Mendelian Randomization – using genetics to separate cause and effect in disease. Genes predispose us to drink more, or less, but most studies are of white European populations. This project will develop statistical tools for drawing causal inference from multi-ethnic biobank comparisons.
Lead Supervisor: Dr Daniel Lawson
The aim of this PhD is to develop appropriate methods to expand the information included on patient benefits in economic evaluation by using measures of health status and capability wellbeing. Such methods will allow for a broader evaluation of patient benefits to aid national health and care decision-making. The successful candidate will be able to draw from a wealth of expertise within the supervisory team, including qualitative and quantitative research methods.
Lead Supervisor: Dr Paul Mitchell
Cleft of the lip and/or palate is a common birth defect which can affect appearance, speech, hearing, dentition and mental health. This PhD will investigate risk of mental health outcomes in cleft and their genetic and non-genetic causes and provides the opportunity to develop into one of few experts globally with in-depth understanding across cleft, genetics, genetic epidemiology and psychiatry.
Lead Supervisor: Dr Evie Stergiakouli
Men who have sex with men (MSM) are at high risk of HIV infection. Behavioural, legal and social factors all contribute to this heightened risk. Epidemiological analysis and modelling will be used to explore the role of context-specific factors (e.g. chemsex in UK, stigma and criminalisation in Africa) in elevating HIV transmission among MSM.
Lead Supervisor: Professor Peter Vickerman
This project aims to further understanding of the acceptability, effectiveness and unintended consequences of the abstinence in pregnancy message. It combines innovative approaches from machine learning, quantitative and qualitative research methods to analyse rich, time-sensitive social media data. This work will directly feed into shaping health promotion campaigns for messages around risks in pregnancy.
Lead Supervisor: Dr Luisa Zuccolo
Prostate cancer is the UK’s most prevalent male cancer. Accurate diagnosis requires invasive biopsy for histological microscopic characterisation. While MRI-based approaches can capture some cell properties, they cannot predict histological morphology, critical to diagnosis/prognosis. This PhD project will use an extremely powerful MRI scanner and develop and test innovative statistical models to characterise prostate microstructure non-invasively as never before.
Lead Supervisor: Dr Leandro Beltrachini
How best to allocate available kidneys to potential recipients on a waiting list is a complex and open problem, to which modern machine learning methods are increasingly being applied. Using data from the UK Renal Registry, this project will focus on improving patient survival, as well as investigating (from a statistical perspective) the ethical implications of possible organ allocation algorithms.
Lead Supervisor: Dr Rhian Daniel
Autism and anorexia nervosa are two distinct clinical conditions that are highly co-occurring. To better understand this overlap, the PhD will investigate the longitudinal association between cognition, autistic traits and anorexia, the shared genetic architecture of both disorders, and the causal links between them.
Lead Supervisor: Dr Catherine Jones
Attention deficit hyperactivity disorder (ADHD) can be defined as a categorical diagnosis or as a continuous trait. Although these definitions are related, we need to better understand how they differ. This PhD will investigate the differences in developmental and clinical outcomes, as well as genetic risk factors in children with ADHD using different definitions.
Lead Supervisor: Dr Kate Langley
Clinical decisions about drug therapy require an understanding of whether treatments are safe and effective. For some medications patients who respond well may also be at increased risk of side-effects. The student will develop statistical methods to explore the links between the benefits and harms of treatments using electronic health record data. This approach will be applied to help identify personalised treatment strategies for patients with Type 2 Diabetes.
Lead Supervisor: Professor William Henley
Cancer and chronic illness pose an enormous burden on population health leading to demand on NHS services and socioeconomic cost. Genomic analysis of cancer to guide personalised therapy is now routine. There is an increased risk of developing cancer when other clinical diagnoses or anomalies in routine blood tests are present. This project will investigate the molecular profile of cancers analysed in routine care and determine the impact of chronic illness.
Lead Supervisor: Professor Chrissie Thirwell