Using advanced machine learning strategies to map early osteoarthritis changes and predict disease progression in preclinical models.

About the Project

I am pleased to offer the opportunity for a self-funded PhD student to join my research team within the Medway School of Pharmacy.

Project Information:

Ageing is associated with a higher risk of developing osteoarthritis. Osteoarthritis is characterised by joint pain, swelling, stiffness and reduced movement leading to long-term disability. The relationship between joint damage and pain is complex. Since patients present in clinics with late-stage osteoarthritis, early detection is challenging. We therefore do not fully understand how early osteoarthritis changes give rise to the chronic condition. We need to find better clinically relevant tools that can identify the biological signatures of osteoarthritis early to fill this crucial gap in our knowledge.

The aim of this project is to identify early biological and structural joint changes associated with osteoarthritis and predict disease progression using advanced machine learning strategies, enabling timely intervention and prevention of disease worsening.

The candidate will use preclinical models of osteoarthritis, cell-based assays, histological analysis, microscopy, three-dimensional X-ray Computed Tomography and data-driven AI modelling to map the early joint changes and predict osteoarthritis progression.

The student will be working within a dynamic team including the lead supervisor Dr Ashraf and collaborators Professor Gianluca Tozzi and Dr Peter Soar from University of Greenwich.

Person Specification:

Applicants should hold a minimum of upper-second class honours degree (UK Bachelor’s level) or overseas equivalent qualification, in a relevant subject discipline, such as cell biology, neuroscience, physiology, pathology, immunology, biomedical engineering or any other related subject area. Prior experience in inflammatory and immune mediated diseases, mathematical modelling and histopathology techniques will be an advantage, but not essential.

The candidate will be given full training in all the above-mentioned techniques by an enthusiastic and supportive supervisor and an experienced supervisory team ensuring that the candidate is fully equipped to undertake this exciting project.

The candidate will be part of a dynamic working environment at Medway School of Pharmacy and the Universities of Kent and Greenwich at the Medway Campus and will have access to all their shared research facilities.