Training cross cutting strand

Leads: Prof Richard Martin & Prof Caroline Relton

Training and career development are integral to our research activities and we are funding two 4-year PhD students and three post-doctoral researchers towards fellowships in integrative epidemiology. Additionally we support, mentor and co-supervise a number of affiliated PhDs and fellowships, including in clinical research. This will support capacity building in a critical area.

Year 1 of the PhD programme is spent undertaking ‘mini-projects’ in causal epidemiology, bioinformatics and molecular sciences, providing the requisite postgraduate level knowledge, intellectual development and technical experience in a broad inter-disciplinary environment. This also provides opportunities for junior academics to supervise projects that contribute to the overall aims of the Programme.  The training programme is integrated with attendance at short courses, including new courses in Epigenetic Epidemiology and Metabolomics & Computational Medicine.

The training leads provide mentorship and integrate the trainees within complementary PhD programmes and postdoctoral support systems, and the broader University research environment to promote integrative approaches and dialogue with collaborators from other disciplines.  Based within the Integrative Epidemiology Unit, students and post-docs have access to a wide mix of expertise and collaborative opportunities.

We organise researcher exchanges with our collaborators (both incoming and outgoing) with co-funding from the University of Bristol’s Institute of Advanced Studies scheme, and fund training visits for early career researchers working on the programme at collaborating institutions.

We currently have 2 PhD students funded on the Integrative Cancer Epidemiology Programme. Ryan Langdon is completing his PhD on “The Effect of HPV Infection on DNA Methylation Patterns in Oropharyngeal Cancer”. James Yarmolinsky is investigating "Identification of modifiable risk factors and mediation in the aetiology of ovarian cancer using novel causal inference methods".


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