PhD Studentship - Developing our Understanding of Active and Healthy Ageing using Biological Assessments

Project advert

Ageing is usually quantified as a measurement of the time elapsed since birth (i.e., Chronological Age). However, this simple count cannot explain the large variations in the ageing trajectories that exist between older people of similar age. Researchers have tried to identify alternative descriptions of ageing based on assessments that reflect the "biological age" of an individual. This involves complex changes occurring in body systems, affected by thousands of genes and their interactions with environments and lifestyles. The research planned within this PhD project will take a data science approach to understand how biological age can be measured and used to describe the ageing process. We will develop metrics to accurately predict biological age with the longer-term goal of making the validated assessments available across very large populations of people for promoting healthy ageing.

Project aims and objectives

• Comprehensive biomarker identification
• Physiological and functional assessments of human adults
• Validation of Biological Age Biomarkers

Specific requirements of the candidate

This is an exciting opportunity for a computer scientist to apply their skillset in applications of human health and ageing. We are seeking an exceptional candidate with a strong background in computer science, particularly one with well-developed analytical skills. Applicants should hold a minimum of an honour’s degree at first or upper second-class level in computer science or related fields.

The research will involve a range of computational assessments, including algorithm development, data analysis, and machine learning applications. Knowledge of the general principles of these areas is essential, and experience with practical implementations and research projects will be highly regarded.

Essential Skills:

• AI and Machine Learning: Deep understanding of AI concepts, machine learning algorithms, and proficiency in frameworks like TensorFlow and PyTorch.
• Data Analysis: Advanced data wrangling, cleaning, and preprocessing skills, with experience in large datasets.
• Programming: Strong skills in Python/Matlab or similar languages, with a focus on clean, efficient, and well-documented code.

Desirable Skills:

• Bioinformatics: Familiarity with biological data and analysis tools, and experience applying AI to healthcare problems.
• Cloud Computing: Knowledge of cloud platforms and big data tools, with experience in distributed computing.
• Statistical Modelling: Understanding of statistical methods for data analysis and model evaluation.

Personal Attributes:

• Problem-Solving
• Passion for AI
• Communication and excellent teamwork

Further information

Interested applicants should contact Fabio Zambolin (f.zambolin@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD based in the Department of Sport and Exercise Sciences (or download the PGR application form).

You should also complete both the (PGR thesis proposal and Narrative CV). The PGR proposal should briefly explain how you see the project developing to address the specific aims and objectives. It is also an opportunity for you to demonstrate how the skills you have map to the area of research and why you see this area as being of importance and interest. 

If applying online, you need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Please note that Home fees are covered. Eligible International students need to make up the difference in tuition fee funding. 

Expected start date: Monday 7^th April 2025

Please quote the reference: SciEng-FZ-2025-AI_AGEX

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