Research Associate in Chemical Kinetics (Fixed-Term)

This post is part of a large pharmaceutical consortium at the interface between academia and industry, where the Fellow will contribute to cutting-edge advancements in reaction understanding and process optimisation. The work will primarily feature the integration of high data-density reaction techniques, laboratory automation & robotics and kinetic/machine learning modelling. Practically, this involves the application of innovative methods such as flow chemistry ramping and high-throughput experimentation to expediate reaction understanding 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 the ChemTech team, you will play a pivotal role in advancing the frontiers of reaction optimisation, automation, and the modelling of chemical data.

This post is offered until 30/04/2027 on a full time basis. 

Key Responsibilities:

• Utilise high data-density reaction techniques, including flow chemistry ramping and high-throughput experimentation, to inform and enhance reaction understanding.
• Employ machine learning and kinetic modelling to analyse complex datasets, extract meaningful insights, and guide the optimisation of chemical reactions.
• Collaborate with internal groups to design and fabricate 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.
• Contribute towards a collegiate working environment, providing hands-on support and getting involved with other PDRAs and PhDs research projects.

About you:

We are looking for a candidate who is:

• Excited about the prospect of interdisciplinary research, as well as “hands-on” regarding lab skills and willing to tinker with equipment.
• Interested in how machine learning and modelling techniques can impact real-world processes.
• Experienced with kinetic analysis and/or coding (MATLAB or Python) - preferred.
• Highly organised with an excellent attention to detail.

Although the right candidate may not have all of these attributes, it is much more important to have the drive and willingness to learn these skills for the fulfilment of the project. However, you must have a PhD or equivalent in a relevant subject area (chemistry/chemical engineering or closely related) or near to completion of a PhD.

What this role offers:

• A friendly, diverse, and supportive working environment.
• Personalised training, both in technical abilities and in career coaching for future academic/industrial opportunities.
• Introductions to a wide collaborative network of academics and industrialists.
• Generous holiday entitlement of 30 days plus standard bank holidays and five university closure days including closure between Christmas and New Year.

Please address any informal enquiries about the role to Dr Connor Taylor (connor.taylor@nottingham.ac.uk). Please note that applications sent directly to this email address will not be accepted.

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