This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science. The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (CDT) is working in partnership with the STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training programme that will equip up to 60 students with: the essential skills needed to become future leaders in distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.
The successful PhD student will be co-supervised by an academic team of experts from chemistry, data science and computing fields, and work with the University’s strategic partner, Unilever, a British, multinational consumer goods manufacturer to capitalise on the information found in existing and new experimental data libraries to develop state of the art data science, AI and machine learning methods to develop and design high performing plastic materials.
High-density polyethylene (HDPE), a semi-crystalline polymer, is widely used in day-to-day life. HDPE can be recycled to produce a post-consumer resin (PCR), which can then be used to make new HDPE products. One of the major issues with using PCR is that it is a variable material. It may contain different grades of plastic differing in molecular weight or flexural strength, it can be contaminated with other materials and the recycling process itself can lead to degradation of the plastic. To more effectively use PCR to recycle HDPE and so avoid the world’s reliance on “virgin” HDPE, we need to understand how the chemical structure controls the performance of the polymer.
Experimental data on recycled plastics exist. These data describe properties of the chemical structure of the plastic (eg the density, degree of crystallinity, degree of ordering of crystals, crystal size and orientation of crystals) alongside quantifications of performance of the plastic (eg stability and crack resistance). The project will focus on the use of Topological Data Analysis (TDA) as a tool that can be used to predict both structural properties and performance from the Chemical description of the polymer(s) present. This, in turn, will make it possible to predict performance from structural properties of the PCR. The existing literature, particularly when augmented to include data related to virgin plastics, is large and descriptions of polymers comprising hundreds or even thousands of atoms are also necessarily high-dimensional. We therefore anticipate the TDA will need to capitalise on High Performance Computing to undertake the analysis at the scale desired.
A practical outcome of the project is to understand how crystallinity changes in recycled HDPE. This understanding could be used in inform the selection of PCR for use in packaging, ultimately increasing the sustainability of plastics in packaging as used extensively worldwide.
The project requires expertise in programming (eg in Python), machine learning and good communication skills.
Home and international students are welcome to apply.
Visit the CDT website for funding and eligibility information.
You must enter the following information to ensure your application is received and processed:
- Admission Term: 2021-22
- Application Type: Research Degree (MPhil/PhD/MD) – Full time
- Programme of Study: Electrical Engineering and Electronics – Doctor in Philosophy (PhD)
The remainder of the guidance is found in the CDT application instructions on our website.