This PhD proposal is a collaborative work between the School of Biomedical Sciences and School of Computing and Engineering at University of West London (UWL) to investigate the feasibility of using AI to predict/optimise the physicochemical and mechanical properties of 3D printed IPC cells scaffolds, thereby reducing the time and data needed for experimentations. Retinal tissue engineering is representing a new approach to treat retinal diseases through the development of a biological substitute; the so-called scaffold. A tissue scaffold is a 3-dimentional (3D) structure with interconnected pores which are used to deliver cells, drugs and genes into the local tissue. Collagen is the most abundant structural protein that play critical role in maintaining the ECM which has been widely used to fabricate scaffolds for tissue engineering because of its biodegradability, superior biocompatibility and weak antigenicity. To fabricate 3D collagen scaffolds with specific shapes and sizes, different methods will be applied such as freeze-drying and 3D bioprinting. Multi-objective optimisations are, however, required to identify suitable freeze-drying and printing conditions and material composition to achieve optimal mechanical and porous constructions properties. This requires extensive experimentation time that is resource demanding. Artificial intelligence (AI) and machine learning (ML) methods have already been applied in tissue engineering and have been shown to be transformative resources to support researchers in the field of regenerative medicine. The ML-based framework takes the material composition and the printing parameters as input to (i) predict printing parameters to optimise the structure’s properties, (ii) assess the quality of the prints and (iii) optimise printability of the material.
You will be working closely with Pharmaceutics, Biomedical scientist as well as Computing and Engineering Scientist to first develop a 3D bioprinting of cells scaffold in Dr Khalili’s research lab [ https://www.uwl.ac.uk/biotherapeutic-drug-development ] and then using of machine learning to design the best printing and scaffold shape in Prof Zolgharni’s research group, Intelligent Sensing and Vision (IntSaV) [ https://www.uwl.ac.uk/research/research-centres-and-groups/intelligent-sensing?redirect=1&source=intsav ].
To this end, a combination of pharmaceutical expertise (freeze-drying, 3D bioprinting, collagen characterisation, cells assays), and machine learning algorithms will be used.
You will be jointly supervised by Dr Hanieh Khalili ([Email Address Removed]) and Prof Massoud Zolgharni ([Email Address Removed]).
- You must have an MPharm or MSc degree (or equivalent experience and/or qualifications) in an area pertinent to the subject area, i.e. Pharmaceutical Science, Pharmacy or Chemistry or AI
- You must have a high standard undergraduate degree at UK 1st class or 2:1 level (or international equivalent)
- You must be fluent in spoken and written English
- Where English is not the applicant’s first language, a minimum IELTS Academic English score of 7.0 overall with a minimum of 6.5 in all components is required.
- You must have excellent communication skills and be able to organise your own work and prioritise work to meet deadlines
- Strong academic track record and practical skills are desired
- Any published scientific papers would be a plus
How to apply
Informal enquiries about the studentship should be addressed to the specific Director of Studies stated above.
Applicants should apply online via our website: www.uwl.ac.uk/research/research-degrees/phd-opportunities
When completing your online application please state the project title in your personal statement. Applications will only be accepted via the online application form.
Applications must include the following:
- Full CV, with a list of any significant course projects and/or industrial experience
- A 2-page research statement indicating what you see are interesting research issues relating to the above PhD topic description and why your expertise is relevant
- Academic transcripts/grades
- A copy of publications of the applicant (if any)