Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning
Area: Medicine
Location: UK Other
Closing Date: Friday 21 November 2025
Reference: MED2038
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 a next‑generation therapeutic with sustained and controlled drug release over a prolonged period, enabling a more stable and efficacious drug delivery compared to conventionally dosed medicine.
This work integrates high data‑density reaction/bioanalysis techniques, laboratory automation & robotics, and machine learning. The project involves innovative high‑throughput experimentation to expedite the synthesis of life‑saving pharmaceuticals – all from sustainable waste streams. It will help reduce suffering worldwide.
The joint studentship is part of the strategic global partnership between University of Nottingham and Adelaide University. You will spend one year at Adelaide University and three years at the University of Nottingham, working with Dr Adam Dundas and Dr Parimala Shivaprasad. As a key member of our teams, you will play a pivotal role in advancing 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 disseminating findings through publications and conferences.
Qualifications
* Completed or nearing completion of a first‑class Master’s in Chemistry, Chemical Engineering, or a related field.
* Background in flow chemistry and/or high‑throughput experimentation, and proficiency in programming languages (Python, MATLAB) used in machine learning applications is desirable – learning can be undertaken during the PhD.
* Excellent communication and interpersonal skills to facilitate collaboration within interdisciplinary research teams.
Application Process
To apply, 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 21 November 2025.
Important funding note
This studentship covers UK home fee only – international students are eligible to apply but must cover the difference in cost for tuition fees. After a suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process.
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