Open PhD position: Data-Driven Reaction Optimisation

Subject area:

Reaction Optimisation, Flow Chemistry, Reaction Engineering, Laboratory Automation, Machine Learning

Overview:

This 36-month funded PhD studentship will contribute to cutting-edge advancements in reaction optimisation through the integration of high data-density reaction techniques, laboratory automation & robotics and kinetic/machine learning modelling. This exciting project involves the application of innovative methods such as flow chemistry ramping and high-throughput experimentation to expediate reaction optimisation in the syntheses of life-saving pharmaceuticals, whilst saving precious reaction material overall. The subsequent data will then be used to populate chemical reaction models to simulate and optimise reactions for the highest yields and purities. 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 reaction optimisation, automation, and the modelling of chemical data.

Key Responsibilities:

Utilise high data-density reaction techniques, including flow chemistry ramping and high-throughput experimentation, to inform and enhance reaction optimisation processes.Employ machine learning and kinetic modelling to analyse complex datasets, extract meaningful insights, and guide the optimisation of chemical reactions.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 Chemistry, Chemical Engineering, or a related field.A background in reaction optimization techniques, 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 Connor.Taylor@nottingham.ac.uk. 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.

Closing Date: 11 Oct 2024 Category: Studentships