PhD studentship: Autonomous Bioactivity Searching (ENG297)

Subject area:

Drug Discovery, Laboratory Automation, Machine Learning

Overview:

This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & 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.

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.Collaborate with internal groups, including the Centre for Additive Manufacturing (CfAM) 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.

Qualifications:

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.

Application Process:

Please note that this is a self-funded PhD opportunity, you must secure your own funding to enrol on this PhD. For students from China, you are encouraged to apply in partnership with the China Scholarship Council - more information can be found here: https://www.nottingham.ac.uk/pgstudy/funding/china-scholarship-council-research-excellence-scholarship

To apply, please submit your CV and a cover letter outlining your research interests and relevant experience to Connor.Taylor@nottingham.ac.uk. The deadline for applicants is the 30th January 2026, but the right candidate may be found and the job opening closed sooner, so you are encouraged to apply sooner.

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. The right candidate will ideally start in October 2026.

Closing Date: 30 Jan 2026
Category: Studentships