PhD Studentship: Machine Learning Driven Reaction Screening in Continuous Flow

Key Skills

Automation, Effective communication skills, Machine learning techniques

Job Description

Closing Date: 29 April 2024 at 23:59

Eligibility: UK Applicants only

Funding

EPSRC Doctoral Training Partnership Studentship offering the award of full academic fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years.

Lead Supervisor: Dr Adam Clayton a.d.clayton@leeds.ac.uk

Co-supervisor: Professor Richard Bourne r.a.bourne@leeds.ac.uk

Project summary

Are you a Chemist or Chemical Engineer looking to have a positive real-world impact on the efficiency of pharmaceutical development? Throughout this project you will develop a multidisciplinary skillset in flow chemistry, organic synthesis, and programming/reactor automation, which you will use to create new approaches for reaction screening and optimisation. In addition, this work will be conducted in collaboration with industrial partners, including multinational pharmaceutical companies.

Rapid transition from drug discovery to manufacturing is critical for the supply of clinical trials and delivery of newly approved medicines. However, traditional workflows often lead to time-consuming and costly redesigns between the different stages of pharmaceutical development. Digitally coupling autonomous reaction screening and process optimisation platforms has the potential to significantly streamline this process through machine learning guided experimentation.

This interdisciplinary project, based across the Schools of Chemistry and Chemical Engineering at the University of Leeds, will investigate flow chemistry and machine learning approaches for the development of an automated reaction screening platform. This platform will be digitally coupled with other autonomous reactors to accelerate the development and optimisation of pharmaceutical processes.

References: None

Entry requirements

First or Upper Second Class UK Bachelor (Honours) or equivalent

Subject Area

Chemical Engineering, Organic Chemistry, Pharmaceutical Chemistry

Keywords

Automation, Chemical Engineering, Machine Learning, Organic Chemistry, Pharmaceutical Development