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Research Fellow in Digital Reaction Engineering, Leeds
Client: University of Leeds
Location: Leeds, United Kingdom
Job Category: -
EU work permit required: Yes
Job Reference: 42056a4c2a3e
Job Views: 6
Posted: 05.05.2025
Expiry Date: 19.06.2025
Job Description:
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 interested in applying your skills in reaction engineering to develop new automated reactor platforms for reaction screening and process optimisation?
Development of synthesis and optimisation of reactions remains a key challenge in pharmaceutical process development, often relying on resource-intensive trial-and-error approaches that are costly, time-consuming, and wasteful. There is a need to develop new digital methods capable of rapidly responding to emerging health challenges.
This EPSRC funded project involves collaboration between the Universities of Leeds (Dr Adam Clayton, Prof. Richard Bourne), Liverpool (Prof. Anna Slater), and Cambridge (Prof. Alexei Lapkin) to create a network of digitally coupled reactors capable of high-throughput screening and self-optimising manufacturing processes. This will involve combining different flow reactor technologies, analytical techniques, and automated workflows to enhance chemical space mapping and generate robust datasets.
Modular experimental platforms will be designed to explore complex mixed variable design spaces efficiently on the microlitre scale. The multisite reactor network will be driven by advanced machine learning algorithms that leverage prior experimental data to increase synthesis success rates and accelerate process development. Collaboration with industry partners will help leverage this workflow to enable lower-cost, sustainable manufacturing of future medicines.
At Leeds, we seek a Research Fellow in Digital Reaction Engineering to develop automated flow platforms for reaction screening and process optimisation. Responsibilities include integrating liquid handling robotics, continuous flow technology, and inline analytical techniques to enable rapid data collection. The role also involves integrating machine learning algorithms for reaction optimisation and mapping the design space, contributing to the multisite reactor network, and applying these technologies to pharmaceutically relevant case studies.
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