Advancing the Frontier of Bespoke In-vivo Orthopaedic Implants

Research theme: Structural Optimisation

How to apply: uom.link/pgr-apply-2425

UK only

This 3.5-year PhD is funded by the Department of Mechanical and Aerospace Engineering and is available to UK-based students. The successful candidate will receive an annual tax-free stipend set at the UKRI amount (£20,780 + Tuition fees for the 2025/2026 academic year). We expect the stipend to increase each year. The prosed start date is 5^th January 2026.

We recommend that you apply early as the advert will be removed once the position has been filled.

The current digital transformation of healthcare, termed ‘Healthcare 5.0,’ leverages technologies like AI, big data analytics, robotics, and digital manufacturing to deliver patient-centric care. This paradigm shift enhances patient outcomes and well-being, with advancements in implant technology playing a crucial role. A significant challenge is the need for costly revision surgeries to upgrade, or repair defective in-vivo implants. For instance, in Total Hip Arthroplasty (THA), invasive surgeries are required to address issues like aseptic loosening and bone remodelling. In 2019, revision surgeries for hip and knee implants cost the UK’s NHS over £60 million. Similarly, implantable electronics like pacemakers and glucose sensors depend on degrading batteries, elevating patient anxiety. To address these issues, there is a growing demand for patient-specific implants with embedded functionalities to provide real-time monitoring of osseointegration and therapeutic benefits for THA patients.

This project proposes designing hip implants with integrated sensors, actuation modules, microprocessors, and high-capacity, extended-life batteries to minimise or eliminate revision surgeries. By employing an interdisciplinary approach, the successful candidate will utilise multiscale modelling, mathematical optimisation, machine learning, and additive manufacturing to create digitally-designed, patient-specific smart implants. To deliver on the project, the successful candidate will develop parametric microscale lattice structures capable of a wide range of mechanical properties (including auxetic and non-linear elastic response). Artificial Neural Networks (ANNs) will be trained to map microarchitectures to mechanical properties. Advanced graphing and optimisation algorithms would allow the intelligent placement, positioning and electric circuitry routing between components within the functionally-graded implant. Implant prototypes will be additively manufactured and tested. The focus will be on developing products that seamlessly integrate emerging technologies to enhance the post-THA patient experience.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact the main supervisor, Dr Chikwesiri  Imediegwu - chikwesiri.imediegwu@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

Advert information