We have 2 fully-funded studentships available for UK “Home” students at the Aston University. Start date 1st October 2022.
Designing biomimetic neural materials for scalable 3D cell
Developing therapies and prevention strategies to tackle neurological diseases such as Alzheimer’s Disease, requires an understanding of the brain cells that are affected. Whilst extensive informative research has been conducted over the past decades using animal models, there remains a significant gap in knowledge surrounding the mechanisms underlying brain cell dysfunction. Recently, there has been a scientific revolution in the field of stem cell-derived neurons, whereby patient’s skin cells can be turned into brain cells in the lab. Bringing the biological methods together with advances in materials, artificial brain cell circuits can be produced to mimic the structure of brain tissues. Whilst stem cells provide scientists with the many different cell types as building blocks to fabricate human neuronal systems, engineering approaches need to be adopted to present these with the necessary three-dimensional complexity of human brain tissue. This project will develop a 3D biomaterial scaffold to display multiple neuro-developmental signaling factors to mimic brain architecture development.
Programming and implementation of a clinical trials visual testing app platform for assessment of advances in tissue engineering
All tissue engineering requires clinical trials prior to licensing and this PhD will develop the tools required to support efficient and detailed clinical ophthalmic metric collection at baseline and at timepoints after treatment. As well as being embedded in the doctoral training centre education and networking, the student will work closely with a start-up medical device company to experience all the stages of medical app development and certification within ISO 13485 to develop a patient management dashboard and modules to conduct visual function tests, utilizing the mobile / smartphones inbuilt camera and sensors. Data analytics will be applied to allow machine learning to aid future clinician decision making for personalized medicine. In addition, the student will develop and clinically validate apps for home monitoring of patients to aid and assess compliance and to give real-work symptomology data.