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Please contact Dr Marc Little for further information ([Email Address Removed])
Context: Fossil-derived polymer-based materials are widely used across various industries, particularly in the production of plastics. Despite growing societal awareness of the environmental and health concerns posed by non-biodegradable plastic waste, the plastics industry continues to expand at a rapid pace. While sustainable alternatives exist, their adoption has been limited. Bio-derived and biodegradable polymers, which are theoretically more environmentally friendly, account for less than 1% of all plastic products due to their higher costs, inferior material properties, and less favourable processing characteristics. This highlights the urgent need to accelerate the discovery and development of more sustainable polymer materials.
Project Overview: We are seeking a highly motivated PhD candidate to join our team at the Edinburgh campus and contribute to an innovative research project focused on polymer degradation. The project aims to develop a novel approach using a fully automated flow chemistry system, integrated with machine learning tools, to accelerate and optimise polymer degradation studies. This research has the potential to significantly impact the field of polymer science and contribute to the development of more sustainable materials. Currently, we use a flow chemistry platform connected to chromatography and spectroscopy systems to monitor the degradation in real time (e.g., J. Flow. Chem. 2023, 13, 103; ACS Macro Lett. 2024, 13, 508). The PhD research will build upon this foundation, leveraging state-of-the-art machine learning tools to automate the process further and generate critical understanding that will drive the design of next-generation sustainable polymers.
The ideal candidate for this position should be interested in polymer materials science and have a degree in Chemistry or a related discipline.
Training and Development: The selected PhD candidate will benefit from comprehensive training in flow chemistry and machine learning, supported by a combination of in-house expertise and external professional opportunities. Additionally, the PhD candidate will have the chance to collaborate closely with industrial partners, fostering an environment where research findings can be swiftly translated into practical, real-world applications.
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