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PhD Studentship: Autonomous Bioactivity Searching


Location:  UK Other
Closing Date:  Friday 31 May 2024
Reference:  ENG1753

Subject area:

Drug Discovery, Laboratory Automation, Machine Learning


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


  • Completed or nearing completion of a Master's degree in Medicinal Chemistry, Chemical Engineering, or a related field.
  • A background in flow 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:

To apply, please submit your CV and a cover letter outlining your research interests and relevant experience to 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. Due to the nature of the funding, only UK applicants can be considered for this position - upon finding the successful candidate, funding is then acquired through University of Nottingham.

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