Available
Project number:
2025_79
Start date:
June 2025
Project themes:
Main supervisor:
Reader in Computational Medicine
Co-supervisor:
Helen Alexander
Additional Information:
Harnessing AI and multi-omics in TwinsUK to unlock genetic and environmental drivers of skin ageing
Background: Skin ageing is influenced by both intrinsic factors (e.g., genetics, age, hormones) and modifiable environmental factors (e.g., UV exposure, smoking, diet). The gut and skin microbiomes also play a role in this process, contributing to changes such as reduced lipid synthesis, dermal thinning, and impaired wound healing, and increases the risk of chronic skin conditions, cancer, and infections. As global life expectancy increases, understanding the mechanisms of skin ageing becomes crucial to mitigating its effects.
Aims: This project leverages the TwinsUK cohort to identify genetic, environmental, and lifestyle factors affecting skin ageing. Using a deep learning model, signs of skin ageing will be classified from a dataset of 7,150 longitudinal portrait photos. The key aims are to: (1) adapt an AI model for skin-ageing classification, (2) assess the relative impact of genetics and environment on skin ageing, (3) identify genetic determinants, and (4) integrate multi-omics data to discover predictive signatures and modifiable risk factors.
Methods: Automated image analysis and AI will be used to classify skin-ageing signs. Clinical, lifestyle, and multi-omic data from the TwinsUK cohort will be integrated using machine learning models. Genetic and environmental factors will be analysed through GWAS and multi-omics integration to identify biomarkers and risk factors. A new data collection, including additional photos and a lifestyle questionnaire, will help replicate findings.
Training: The student will receive training in AI, machine learning, and multi-omics integration, focusing on identifying actionable factors to delay skin ageing.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at