Triangulating evidence to uncover cardiovascular disease causes and treatment opportunities: Integrating individual participant data meta-analysis, deep learning, and genetic epidemiology (Prof Kazem Rahimi)
Triangulating evidence to uncover cardiovascular disease causes and treatment opportunities: Integrating individual participant data meta-analysis, deep learning, and genetic epidemiology.
DESCRIPTION OF PROJECT
The Deep Medicine Research Group at the Nuffield Department of Women's and Reproductive Health (NDWRH), University of Oxford, is inviting applications for a DPhil position focused on advancing our understanding of cardiovascular diseases and their causal risk factors using cutting-edge techniques in genetic epidemiology, IPD meta-analysis and deep learning. This is an exceptional opportunity for a talented and motivated researcher to contribute to groundbreaking research in the field of epidemiology and cardiovascular diseases.
Cardiovascular diseases pose a significant global health burden, and uncovering their causal risk factors is imperative for effective prevention and treatment strategies. This multidisciplinary project will delve into the realm of genetic epidemiology, AI, IPD meta-analysis, and causal inference to decipher the intricate interplay between genetics, modifiable and non-modifiable risk factors, and various cardiovascular diseases. By leveraging vast repositories of big data, the project aims to identify novel insights that can revolutionize our understanding of the prevention and treatment of these diseases.
The successful candidate will undertake an in-depth investigation using state-of-the-art methodologies. The project offers a unique opportunity to explore causal inference methodologies to unravel the complex relationships between genetic factors, risk variables, and cardiovascular outcomes. Additionally, the use of deep learning techniques will enable the extraction of hidden patterns, predictive models and risk factors that contribute to the advancement of precision medicine.
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 cutting-edge research. You will be part of a vibrant academic community, collaborating with experts in genetics, epidemiology, and AI. The NDWRH offers a supportive and inclusive research environment that fosters intellectual growth and interdisciplinary collaboration.
- A strong academic background in a relevant field such as epidemiology, biostatistics, genetics, bioinformatics, or a related discipline.
- Proficiency in programming and data analysis, with experience in statistical software.
- Familiarity with electronic health records and large-scale data manipulation and analysis.
- Excellent communication skills and the ability to work collaboratively in a multidisciplinary research environment.
The DPhil candidate possessing proficiencies in AI, genetic epidemiology, statistics, or closely related domains, will be provided with advanced training encompassing AI modelling for electronic health records (EHR), genetic epidemiology, IPD meta-analysis, and diverse data modalities accessible for exploration within the realm of Deep Medicine.
Furthermore, the candidate will be equipped with expertise in executing comprehensive literature reviews, writing scientific papers for peer-reviewed journals and conferences, and engaging collaboratively within a robust interdisciplinary cohort comprising AI experts, epidemiologists, cardiologists, and statisticians.
This collaborative cohort aims to orchestrate meticulous doctoral research of the highest calibre.
Unfunded. The student will need to bring their own funding.
The Deep Medicine group will assist the DPhil candidate in securing funding through fellowship/studentship.
HOW TO APPLY
To apply for this research degree, please click here.