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Developing our Understanding of Active and Healthy Ageing Using Biological Assessments

  • DeadlineDeadline: Monday 13 January 2025
  • North West, All EnglandNorth West, All England

Description

To advance our research into human health and ageing, we are looking for PhD candidates with a computer science and machine learning background. We particularly welcome applications from those with knowledge of medical image analysis and/or data science. Ageing is usually quantified as a measurement of the time elapsed since birth (chronological age). However, this simple count cannot explain the large variations in the ageing trajectories that exist between older people of similar age. For these reasons, 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. This will have an important impact on our society by raising the quality of life of older people living in our communities.

Project aims and objectives 

  1. Comprehensive biomarker identification: The first phase involves conducting a review of existing literature, examining various biomarkers that have been used to assess biological age, and interrogating large existing databases (such as UK Biobank) to investigate potential biomarkers in this context.
  2. Physiological and functional assessments of human adults: This phase of the project will establish a unique dataset by recruiting human volunteers to complete assessments using advanced 3T magnetic resonance imaging to investigate the ageing brain and other body systems, as well as assessments of epigenetic changes to the DNA as biomarkers of biological age.
  3. Validation of biological age biomarkers: The third phase of the project will validate a short-form of the assessments from Phase 2 and develop an AI model and algorithm-driven approach that can be implemented for future, large-scale studies.

Entry Requirements

This is an exciting opportunity for computer scientists to apply their skillsets in applications of human health and ageing. We are seeking exceptional candidates with a strong background in computer science, particularly those 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.

We are looking for proactive, independent, and enthusiastic individuals with a critical mindset to play a pivotal role in this cutting-edge research project. The appointed person will be based in Manchester as part of our research team. The candidate will have access to our state-of-the-art facilities of the Institute of Sport and the new cutting-edge £117M Dalton Building, being part of our growing doctoral research community.

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: Strong analytical skills and innovative thinking.
  • Passion for AI: Enthusiasm for using AI to improve healthcare.
  • Communication: Excellent teamwork and ability to convey technical concepts clearly

Fees

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

How To Apply

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 will need to upload your statement in the supporting documents section or email the application form and statement to PGRAdmissions@mmu.ac.uk.

Closing date: Monday 13 January 2025

Expected start date: Monday 7 April 2025

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

Who is eligible to apply?

UK, EU and International applicants

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