Triangulating evidence to uncover the causes of cardiovascular disease and identify treatment opportunities: Integrating clinical trials, omics, and large-scale observational evidence (Professor Kazem Rahimi)
PROJECT TITLE
Triangulating evidence to uncover the causes of cardiovascular disease and identify treatment opportunities: Integrating clinical trials, omics, and large-scale observational evidence
SUPERVISORS
DESCRIPTION OF PROJECT
Despite decades of research and advancements in medical science, cardiovascular diseases continue to pose a significant global health burden. Many cardiovascular conditions lack effective pharmacological treatment options, and those with available treatments often remain suboptimally controlled. Therefore, identifying new causal risk factors, cost-effective treatment strategies, and therapeutic opportunities is essential. This multidisciplinary project will explore the field of randomised controlled trials, employing omics data for causal inference, drug target discovery and validation, and utilising large-scale observational evidence to unravel the complex interplay of factors influencing cardiovascular diseases. By leveraging various big data sources, the project aims to generate novel insights that could transform our understanding of the prevention and treatment of these conditions.
The ideal candidate needs to have an academic background or MSc-level experience in a relevant field, such as epidemiology, clinical trial, biostatistics, genetics, bioinformatics, cardiovascular medicine, or related disciplines. Proficiency in statistical programming (R, Python, Stata) and data analysis are preferred. Basic knowledge of clinical trial design, electronic health records or large-scale data manipulation and analysis is also desirable. Excellent communication skills and the ability to work collaboratively in a multidisciplinary research environment are required.
TRAINING OPPORTUNITIES
As a DPhil student within the DeepMedicine Research Group, you will have access to state-of-the-art facilities and resources at the University of Oxford, a globally renowned institution for pioneering research. You will be part of a vibrant academic community, collaborating with experts in genetics, epidemiology, clinical medicine and AI. The NDWRH provides a supportive and inclusive research environment that fosters intellectual growth and interdisciplinary collaboration.
The project offers training in a wide range of topics, including those applicable to both clinical research and general personal development. The DPhil candidate will receive training in clinical trial execution, design, and analysis; IPD data meta-analysis methods; analysis of OMICs dataset; as well as causal inference approaches using observational big data.
Funding Information
The position is not currently funded; therefore, the candidate will need to secure funding. The University of Oxford provides some competitive DPhil scholarships that students can apply for in parallel. Additionally, the supervision team can guide and support the candidate in preparing and applying for DPhil fellowships from external funding bodies.
HOW TO APPLY
To apply for this research degree, please click here.