Research Fellow in Digital Reaction Engineering

Are you an experienced and ambitious researcher looking for your next challenge? Do you want to further your career in one of the UK’s leading research-intensive Universities? Are you looking to apply your skills in reaction engineering to the development of new automated reactor platforms for reaction screening and process optimisation?

Development of synthesis and optimisation of reactions remain rate-limiting factors in pharmaceutical process development, often relying on resource-intensive trial-and-error approaches that are costly, time-consuming, and wasteful. This highlights the need to develop new digital methods that are capable of rapidly responding to emerging health challenges.

In this EPSRC funded project, we will combine expertise across the Universities of Leeds, Liverpool, and Cambridge to create a network of digitally coupled reactors, capable of high-throughput screening and self-optimising manufacturing processes. This will be achieved by combining different flow reactor technologies, analytical techniques, and automated workflows to provide enhanced mapping of chemical space and generation of robust high-quality datasets.

Modular experimental platforms will be designed, capable of efficiently exploring complex mixed variable design spaces on the microlitre scale. Our multisite reactor network will be driven by next generation machine learning algorithms, which will use knowledge from prior experimental campaigns to increase library synthesis success rates and accelerate the development and optimisation of chemically related processes.

In collaboration with our partners in the pharmaceutical industry, we will leverage this novel workflow to accelerate lower cost and more sustainable manufacturing of future medicines.

At Leeds, we are seeking a Research Fellow in Digital Reaction Engineering to develop automated flow platforms for reaction screening and process optimisation. You will integrate liquid handling robotics, continuous flow technology and inline analytical techniques to enable rapid collection of process relevant data. This will require integration of machine learning algorithms for reaction optimisation and mapping of the design space. You will work alongside other members of the team to add these capabilities to a multisite reactor network and apply this technology towards pharmaceutically relevant case studies.