Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.


Ageing contributes to many diseases that affect all organ systems. Chronological age (CA) is typically measured in years and tracked by birthdays. However, life expectancy varies considerably among individuals with equal or similar CAs due to differences in their genes, living environments and lifestyles. Biological age (BA) quantifies the ageing of our body's functions rather than simply measuring the passage of time and could inform risk prediction and development of new treatments. Molecular biomarkers that reflect the biological age of a cell type, tissue, organ (such as the heart or brain) or whole organism are needed to characterise BA. Recent advances in high-throughput multi-omics methods are enabling the characterization and quantification of thousands of novel biomarkers that have the potential to provide new insights into the aging process.

The project will be based on the prospective UK Biobank and China Kadoorie Biobank, with detailed genetic, metabolomic and proteomic data linked to a wide range of disease outcomes. The main aims are to characterise and validate aging clocks and to assess consequences of aging. These complex and high dimensional data require the use of sophisticated statistical approaches to identify relevant markers of biological aging and to characterise and construct metrics reflecting 'ageing clocks'. 


The specific area of DPhil project will be developed according to the candidate’s interests and aptitude, and may include some of the following objectives: 

  • Using machine learning methods to develop, characterise, and validate different “ageing clocks” based on conventional markers (e.g. lipids, inflammation, adiposity, blood pressure) commonly measured in a clinical setting; 
  • Using machine learning methods to build, characterise, and validate different “ageing clocks” based on novel biomarkers (e.g. proteomics and metabolomics), and to compare with conventional “ageing clocks”; 
  • To identify the “age gap” or rate of aging for several chronic diseases within and between European and Chinese populations;
  • To assess the utility of different “aging clocks” in predicting fatal and non-fatal major disease outcomes in European and Chinese adults. 


There will be in-house training in epidemiology, statistics, data science and genetics, plus, if required, attendance of external training courses. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large-scale high-dimensional data using state-of-art techniques, and to report research findings, including conference presentations and publications as the lead author in peer-reviewed journals.


Candidates should have postgraduate training in medical statistics, computer science, engineering or a closely related subject. Proficiency with statistical software and strong programming skills are essential.