PhD Studentship: Autonomous Bioactivity Searching

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

Subject area:

Drug Discovery, Laboratory Automation, Machine Learning

Overview:

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.

Qualifications:

• 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 [email protected] . 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.