PhD studentship: Waste to Medicine (MED2038)

Subject area:

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

Overview:

This highly interdisciplinary 4-year 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. 

This joint studentship is part of the strategic global partnership between University of Nottingham and Adelaide University. As part of the project, you will spend one year at Adelaide University and three years at the University of Nottingham. 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 1st 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 languages (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. The deadline for applications is the 21st November 2025.

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.

Important funding note:

This studentship covers UK home fees only - international students are eligible to apply but must cover the difference in cost for the tuition fees. After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process.

Closing Date: 21 Nov 2025
Category: Studentships