PhD Studentship: Waste to Medicine

Subject area:

Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning

Overview:

This highly interdisciplinary 36-month funded PhD studentship will contribute to cutting-edge advancements in automated drug discovery and bio-instructive material manufacture. The project aims to utilise flower waste as a sustainable feedstock to discover new bioactive small molecules, then encapsulate and embed these molecules into well-defined, injectable microparticles. This is one example of next-generation therapeutics, with a sustained and controlled drug release over a prolonged period, enabling a more stable and efficacious drug delivery over conventionally dosed medicine.

This work integrates high data-density reaction/bioanalysis techniques, laboratory automation & robotics and machine learning. The project involves the application of innovative methods such as high-throughput experimentation to expediate the syntheses of life-saving pharmaceuticals – all from sustainable waste streams. This project will help to make a substantial difference towards automated drug discovery and helping to reduce suffering worldwide. 

The research will be conducted using state-of-the-art equipment, including both commercial tools and bespoke in-house apparatus, in collaboration with Dr Adam Dundas and Dr Parimala Shivaprasad. As a key member of our teams, you will play a pivotal role in advancing the frontiers of sustainable drug discovery and delivery.

Key Responsibilities:

• Utilise high data-density reaction/bioanalysis techniques, including high-throughput experimentation, to inform and enhance drug optimisation.
• Employ machine learning to analyse complex datasets, extract meaningful insights, and guide the optimisation of drug molecules.
• Contribute to interdisciplinary research efforts, fostering collaboration between various research groups, and actively participate in the dissemination of findings through publications and conferences.

Qualifications:

• Completed/nearing completion of a 1^st Class Master's in Chemistry, Chemical Engineering, or a related field.
• A background in flow chemistry (and/or high-throughput experimentation), as well as proficiency in programming laguages (Python/MATLAB) commonly used in machine learning applications, is desirable but learning can be completed during the PhD.
• Excellent communication and interpersonal skills to facilitate collaboration within interdisciplinary research teams.

Application Process:

To apply, please submit your CV and a cover letter outlining your research interests and relevant experience to Connor.Taylor@nottingham.ac.uk. Please also contact this email for further information and an informal discussion regarding the PhD.

This is an excellent opportunity for an enthusiastic graduate to build a strong skillset in interdisciplinary research and a collaborative network with both academic and industrial partners at an international level.

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