Raha Pazok MD PhD FHEA is a medical doctor and an epidemiologist. She studied Epidemiology at the Netherlands Institute for Health Sciences (NIHES) and in the University of Amsterdam. She worked with various cohort and case control studies such as the Arrhythmia Genetics in the Netherlands (AGNES), the Rotterdam Study, the Airwave Health Monitoring Study and the UK Bio bank. In 2016, she joined the Department of Epidemiology and Bio-statistics at Imperial College London as a Research Associate. In 2020, she started a Teaching & Research academic position at Brunel University London. Dr Pazoki specializes in the field of health data research, with a primary focus on the epidemiology of cardiometabolic diseases. She holds a particular interest in exploring causal inference and precision medicine by leveraging genomics and extensive health data sets with sample sizes exceeding 500,000 individuals. Her expertise spans various domains, including precision medicine, global health, interventions, and the application of artificial intelligence for predicting health outcomes. She harbors a keen interest in identification of the relationship between circulating molecules and biomarkers, nutrition, lifestyle choices, genetic factors, and their collective contribution to the modulation of health risk factors and outcomes. She was the first to identify 517 novel genetic loci associated with liver enzymes and the first to show the causal effect of liver dysfunction on cardiovascular diseases. In addition, she is the first to show the effect of the alcohol consumption WDPCP gene in lipid metabolism, and liver cirrhosis. Available PhD projects: Exploring artificial Intelligence for precision medicine, focusing on the interplay between gene and environemnt Cardiovascular diseases, including hypertension, myocardial infarction, heart failure, and stroke, remain the leading causes of mortality worldwide and their prevalence continues to rise. These conditions are influenced by a combination of modifiable (e.g., diet, physical inactivity, smoking) and non‑modifiable (e.g., age, sex, genetic background) risk factors. Genetic variation, in particular, plays an important role in differentiating individuals at high and low risk, enabling more precise and targeted prevention strategies. As precision medicine advances, artificial intelligence (AI) and data‑driven approaches offer powerful tools for integrating genetic, environmental, and lifestyle information to better predict disease risk and improve population health outcomes.This umbrella project brings together a series of PhD opportunities focused on applying AI‑enabled precision‑medicine approaches to understand how genetic and environmental factors, such as alcohol consumption, smoking, mental‑health‑related exposures, and other lifestyle variables, contribute to cardiovascular diseases. Using data from the UK Biobank comprising 500,000 participants, students will employ statistical and machine‑learning methods to conduct advanced analyses at scale.
Cardiovascular diseases, including hypertension, myocardial infarction, heart failure, and stroke, remain the leading causes of mortality worldwide and their prevalence continues to rise.
These conditions are influenced by a combination of modifiable (e.g., diet, physical inactivity, smoking) and non‑modifiable (e.g., age, sex, genetic background) risk factors. Genetic variation, in particular, plays an important role in differentiating individuals at high and low risk, enabling more precise and targeted prevention strategies.
As precision medicine advances, artificial intelligence (AI) and data‑driven approaches offer powerful tools for integrating genetic, environmental, and lifestyle information to better predict disease risk and improve population health outcomes. This umbrella project brings together a series of PhD opportunities focused on applying AI‑enabled precision‑medicine approaches to understand how genetic and environmental factors, such as alcohol consumption, smoking, mental‑health‑related exposures, and other lifestyle variables, contribute to cardiovascular diseases.
Using data from the UK Biobank comprising 500,000 participants, students will employ statistical and machine‑learning methods to conduct advanced analyses at scale. If interested, please contact Dr Pazoki to select specific project area that interests you.
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the "Apply here" button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
Good luck!
This is a self-funded topic
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.