Investigating Rare Cardiovascular Diseases through Genetic Epidemiology and Observational Study of Electronic Health Records Data (Prof Kazem Rahimi)
PROJECT TITLE
Investigating Rare Cardiovascular Diseases through Genetic Epidemiology and Observational Study of Electronic Health Records Data
SUPERVISORS
DESCRIPTION OF PROJECT
The DeepMedicine Research Group at the University of Oxford's NDWRH Department is pleased to announce a DPhil position that focuses on advancing our understanding of rare cardiovascular diseases through the integration of observational analysis of electronic health records data and genetic epidemiology.
Project Focus:
Cardiovascular diseases (CVDs) remain a global health challenge, encompassing a wide spectrum of disorders with varying prevalence and clinical presentations. This DPhil project will specifically concentrate on rare cardiovascular diseases, a subset that poses significant diagnostic and therapeutic challenges due to their limited occurrence and distinct genetic underpinnings.
Genetic Epidemiology:
Understanding the risk factors of rare cardiovascular diseases is paramount for the field of cardiovascular medicine. The successful candidate will engage in comprehensive genetic epidemiology research, delving into the identification of causal risk factors associated with these conditions, using techniques such as Mendelian randomisation and genome-wide association studies.
Observational Study using Electronic Health Records Data:
Electronic health records (EHR) have emerged as a valuable resource for medical research, offering longitudinal patient data that provide insights into disease progression, treatment outcomes, and real-world clinical practices. In this project, you will harness the power of EHR data to conduct robust observational studies. By mining and analysing large-scale datasets, you will decipher patterns, risk factors, and clinical trajectories associated with rare cardiovascular diseases. This empirical approach will enhance our comprehension of the diseases' heterogeneity and inform strategies for improved patient care.
Interdisciplinary Collaboration:
As part of the DeepMedicine Research Group, you will collaborate with esteemed researchers from diverse backgrounds, including epidemiology, cardiology, and AI. This collaborative environment encourages cross-pollination of ideas, fostering innovation and holistic problem-solving.
Qualifications:
We welcome applications from candidates with a strong background in epidemiology, genetics, biostatistics, or related fields. Proficiency in data analysis and programming languages (such as R or Python) is essential. Prior experience with EHR data analysis and familiarity with genetic analysis tools will be advantageous.
TRAINING OPPORTUNITIES
The DPhil candidate will be provided with advanced training encompassing analysis of electronic health records (EHR), genetic epidemiology, and conventional epidemiological approaches such as nested case-control studies. 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
The student will be able to access the training schemes and courses provided for graduate students by the University of Oxford.
Funding information
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.