Autonomous bioactivity searching 

This 36-month PhD will contribute to cutting-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation and robotics, and machine learning modelling. This exciting project involves the application of innovative methods such as high-throughput experimentation to expediate the syntheses - and bioanalysis - of life-saving pharmaceuticals. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims to greatly accelerate bioactive molecule discovery and significantly reduce costs in drug discovery, enabling new drug targets that are currently economically unfeasible such as in rare and poverty-related diseases. 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. As a key member of our team, you will play a pivotal role in advancing the frontiers of drug discovery, laboratory automation, and the modelling of chemical data.

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. 

PhD start date: ideally October 2026 

Key responsibilities 

• Use 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
• Collaborate with internal groups including the Centre for Additive Manufacturing to design and fabricate (3D print) bespoke equipment tailored to the project's specific needs
• Contribute to interdisciplinary research efforts fostering collaboration between various research groups, and actively participate in the dissemination of findings through publications and conferences

Candidate requirements

• Completed or nearing completion of a master’s degree in medicinal chemistry, chemical engineering, or a related field
• A background in organic chemistry and/or high-throughput experimentation is desirable
• 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

Funding

Please note that this is a self-funded PhD opportunity, you must secure your own funding to enrol on this PhD. Explore funding options for postgraduates.

Students from China are encouraged to apply in partnership with the China Scholarship Council. See the China Scholarship Council Research Excellence Scholarship. 

Eligibility and how to apply

Open to UK, EU and international candidates.

To apply please submit your CV and a cover letter outlining your research interests and relevant experience to Dr Connor Taylor via Connor.Taylor@nottingham.ac.uk.

Deadline: 30 January 2026. You are encouraged to apply early. The PhD could be closed sooner if the right candidate is found.